studentized

The S63 draft was also fairly large, so the league saw a good group of low-TPE guys entering the VHL that season. I'd be willing to bet this evened out depth a bit and made it so teams weren't just the same three elite guys repeatedly putting the puck in the net.
 
Since then, too, draft classes have been good-sized--the S66 class was historically big, and we've seen classes at or near that level since.

Nice article! As someone who was there, I have a couple of reasons I feel that could have contributed to the windfall of goals. 
 
1) Team setups - It wouldn't surprise me if some of the teams in S62 had the largest SC:PA ratio in the VHL history. Lots of the players on the rebuilding SEA and RIG teams were sitting on 40 passing, but still had 90+ scoring, and this effect on the decision making formula likely, combined with the DEF drop you mentioned, led to the huge amount of shots. 
 
2) Goalies -   The goalies took a real hit in S62 in comparison to S61. In Season 61, the average overall goalie was 73.9 Overall, and this went WAY down to 70.1 Overall in Season 62 before going back up to 73.4 Overall in Season 63. The dip in goalie talent combined with the greater number of shots spelled disaster for a lot of teams. 

I had a hard time coming up with an idea for theme week this season. I usually like to incorporate a bit of data in my media spots, but the history of the VHL is already pretty well documented. Initially, I thought I would do some era-adjusted stat comparisons between current players vs those of the past, but collecting player stats prior to season 59 is still on my to-do list, and there wasn’t a whole lot of interesting player comparisons I could find with what I had. It wasn’t all a waste however, since all that work did eventually lead me to something I did want to write about, albeit with not a lot of time left to actually do the writing.
 
The chart below plots the scoring output for how the league-average player performed across each season from S59 to S68. It may not look like there is a huge difference (in hindsight, comparing the “league-average player” across seasons may not be the best way to view what I want to show), but the effects become more obvious when looking at some of the stat leaders; S62 was by far the craziest.

 
 
 
I was already somewhat aware about the high scoring S60-S63 seasons, just from chatter around the forum, but what I never really understood was what caused it. Why did scoring come back down to earth in S64 and plateau? What caused it to go up in the first place? Naively I just assumed that the SHTS sim has lots of knobs that can be tweaked and that some knob was probably turned in such a way that made scoring increase. While possible, that answer is highly unsatisfying. More realistically, there is probably something in the data that can explain it. I had a couple of ideas about what types of things I would think would cause a scoring increase in the VHL.
 
For example, maybe the ratio of the number of user-created forwards to the number of defencemen/goalies was more in favour of forwards? If there weren’t a lot of people that chose to make players in goal-preventing positions, then it would make sense to see scoring go up. The chart below plots this information. As you can see, this mix of positions has pretty much been left unchanged across the years. In particular nothing here stands out about S62 that would explain it's league scoring rates.
 
Rather than looking at the quantity of players at each position, we should really be looking at the quality. It still feels like TPA is the most common way that people measure the quality of a player on the forums, so that’s what I used. Below plots the average TPA invested in each player at each position group (forward, defence, or goalie). S62 clearly looks like the outlier here, with low average TPA across all position groups. Most notably it was the worst goalie crop we’ve seen in a while, mostly due to retirement of the elite guys from the year before. But in terms of the gap between TPA in forwards vs TPA in defence vs TPA in goalie, there really doesn’t seem to be a clear explanation. In fact, the TPA gap favours forwards in more in recent years than at any other point in my data, yet league scoring hasn’t returned to it’s past high.
 
 

                                                                  Number of User Players By Position                                                           Average TPA of Players By Position
 
One obvious flaw to the above line of reasoning is that it doesn’t account for where the TPA was spent. There are definitely high TPA forwards that don’t score much (Current Riga forward Anthony Matthews has the highest TPA “cost” per goal scored in all my data). And there are also high TPA defensemen that don’t prevent scoring well (this is harder to measure, obviously, but the most notable high TPA, high minus player from my data was Luc-Pierre Lespineau-Lebrunette. What a fighter). To account for this without getting too complicated we might only want to look at two TPE stat categories; SC as the strongest predictor of goal scoring and DF as the (most-likely) strongest predictor of suppressing goal-scoring. Below the chart plots the average rating of SC vs DF across seasons. One thing this shows is how investment into DF changed after S63. Despite SC increasing on pace with DF in recent years, it seems that just the presence of DF being invested in played a role in moving past the high scoring early 60’s.
 

 
In my mind, there’s still a lot left unexplained in regards to league-wide scoring variations across seasons in the sim. What I am more confident in saying is that the early S60’s were likely an anomaly; a period where the average player was worse than we’ve seen in a long time, where people didn’t care as much about DF as we currently do, and where the few good players that did exist were able to take advantage and score at an insane rate. I doubt we see something quite like it again.

I had a hard time coming up with an idea for theme week this season. I usually like to incorporate a bit of data in my media spots, but the history of the VHL is already pretty well documented. Initially, I thought I would do some era-adjusted stat comparisons between current players vs those of the past, but collecting player stats prior to season 59 is still on my to-do list, and there wasn’t a whole lot of interesting player comparisons I could find with what I had. It wasn’t all a waste however, since all that work did eventually lead me to something I did want to write about, albeit with not a lot of time left to actually do the writing.
 
The chart below plots the scoring output for how the league-average player performed across each season from S59 to S68. It may not look like there is a huge difference (in hindsight, comparing the “league-average player” across seasons may not be the best way to view what I want to show), but the effects become more obvious when looking at some of the stat leaders; S62 was by far the craziest.

 
 
 
I was already somewhat aware about the high scoring S60-S63 seasons, just from chatter around the forum, but what I never really understood was what caused it. Why did scoring come back down to earth in S64 and plateau? What caused it to go up in the first place? Naively I just assumed that the SHTS sim has lots of knobs that can be tweaked and that some knob was probably turned in such a way that made scoring increase. While possible, that answer is highly unsatisfying. More realistically, there is probably something in the data that can explain it. I had a couple of ideas about what types of things I would think would cause a scoring increase in the VHL.
 
For example, maybe the ratio of the number of user-created forwards to the number of defencemen/goalies was more in favour of forwards? If there weren’t a lot of people that chose to make players in goal-preventing positions, then it would make sense to see scoring go up. The chart below plots this information. As you can see, this mix of positions has pretty much been left unchanged across the years. In particular nothing here stands out about S62 that would explain it's league scoring rates.
 
Rather than looking at the quantity of players at each position, we should really be looking at the quality. It still feels like TPA is the most common way that people measure the quality of a player on the forums, so that’s what I used. Below plots the average TPA invested in each player at each position group (forward, defence, or goalie). S62 clearly looks like the outlier here, with low average TPA across all position groups. Most notably it was the worst goalie crop we’ve seen in a while, mostly due to retirement of the elite guys from the year before. But in terms of the gap between TPA in forwards vs TPA in defence vs TPA in goalie, there really doesn’t seem to be a clear explanation. In fact, the TPA gap favours forwards in more in recent years than at any other point in my data, yet league scoring hasn’t returned to it’s past high.
 
 

                                                                  Number of User Players By Position                                                           Average TPA of Players By Position
 
One obvious flaw to the above line of reasoning is that it doesn’t account for where the TPA was spent. There are definitely high TPA forwards that don’t score much (Current Riga forward Anthony Matthews has the highest TPA “cost” per goal scored in all my data). And there are also high TPA defensemen that don’t prevent scoring well (this is harder to measure, obviously, but the most notable high TPA, high minus player from my data was Luc-Pierre Lespineau-Lebrunette. What a fighter). To account for this without getting too complicated we might only want to look at two TPE stat categories; SC as the strongest predictor of goal scoring and DF as the (most-likely) strongest predictor of suppressing goal-scoring. Below the chart plots the average rating of SC vs DF across seasons. One thing this shows is how investment into DF changed after S63. Despite SC increasing on pace with DF in recent years, it seems that just the presence of DF being invested in played a role in moving past the high scoring early 60’s.
 

 
In my mind, there’s still a lot left unexplained in regards to league-wide scoring variations across seasons in the sim. What I am more confident in saying is that the early S60’s were likely an anomaly; a period where the average player was worse than we’ve seen in a long time, where people didn’t care as much about DF as we currently do, and where the few good players that did exist were able to take advantage and score at an insane rate. I doubt we see something quite like it again.

I had a hard time coming up with an idea for theme week this season. I usually like to incorporate a bit of data in my media spots, but the history of the VHL is already pretty well documented. Initially, I thought I would do some era-adjusted stat comparisons between current players vs those of the past, but collecting player stats prior to season 59 is still on my to-do list, and there wasn’t a whole lot of interesting player comparisons I could find with what I had. It wasn’t all a waste however, since all that work did eventually lead me to something I did want to write about, albeit with not a lot of time left to actually do the writing.
 
The chart below plots the scoring output for how the league-average player performed across each season from S59 to S68. It may not look like there is a huge difference (in hindsight, comparing the “league-average player” across seasons may not be the best way to view what I want to show), but the effects become more obvious when looking at some of the stat leaders; S62 was by far the craziest.

 
 
 
I was already somewhat aware about the high scoring S60-S63 seasons, just from chatter around the forum, but what I never really understood was what caused it. Why did scoring come back down to earth in S64 and plateau? What caused it to go up in the first place? Naively I just assumed that the SHTS sim has lots of knobs that can be tweaked and that some knob was probably turned in such a way that made scoring increase. While possible, that answer is highly unsatisfying. More realistically, there is probably something in the data that can explain it. I had a couple of ideas about what types of things I would think would cause a scoring increase in the VHL.
 
For example, maybe the ratio of the number of user-created forwards to the number of defencemen/goalies was more in favour of forwards? If there weren’t a lot of people that chose to make players in goal-preventing positions, then it would make sense to see scoring go up. The chart below plots this information. As you can see, this mix of positions has pretty much been left unchanged across the years. In particular nothing here stands out about S62 that would explain it's league scoring rates.
 
Rather than looking at the quantity of players at each position, we should really be looking at the quality. It still feels like TPA is the most common way that people measure the quality of a player on the forums, so that’s what I used. Below plots the average TPA invested in each player at each position group (forward, defence, or goalie). S62 clearly looks like the outlier here, with low average TPA across all position groups. Most notably it was the worst goalie crop we’ve seen in a while, mostly due to retirement of the elite guys from the year before. But in terms of the gap between TPA in forwards vs TPA in defence vs TPA in goalie, there really doesn’t seem to be a clear explanation. In fact, the TPA gap favours forwards in more in recent years than at any other point in my data, yet league scoring hasn’t returned to it’s past high.
 
 

                                                                  Number of User Players By Position                                                           Average TPA of Players By Position
 
One obvious flaw to the above line of reasoning is that it doesn’t account for where the TPA was spent. There are definitely high TPA forwards that don’t score much (Current Riga forward Anthony Matthews has the highest TPA “cost” per goal scored in all my data). And there are also high TPA defensemen that don’t prevent scoring well (this is harder to measure, obviously, but the most notable high TPA, high minus player from my data was Luc-Pierre Lespineau-Lebrunette. What a fighter). To account for this without getting too complicated we might only want to look at two TPE stat categories; SC as the strongest predictor of goal scoring and DF as the (most-likely) strongest predictor of suppressing goal-scoring. Below the chart plots the average rating of SC vs DF across seasons. One thing this shows is how investment into DF changed after S63. Despite SC increasing on pace with DF in recent years, it seems that just the presence of DF being invested in played a role in moving past the high scoring early 60’s.
 

 
In my mind, there’s still a lot left unexplained in regards to league-wide scoring variations across seasons in the sim. What I am more confident in saying is that the early S60’s were likely an anomaly; a period where the average player was worse than we’ve seen in a long time, where people didn’t care as much about DF as we currently do, and where the few good players that did exist were able to take advantage and score at an insane rate. I doubt we see something quite like it again.

I had a hard time coming up with an idea for theme week this season. I usually like to incorporate a bit of data in my media spots, but the history of the VHL is already pretty well documented. Initially, I thought I would do some era-adjusted stat comparisons between current players vs those of the past, but collecting player stats prior to season 59 is still on my to-do list, and there wasn’t a whole lot of interesting player comparisons I could find with what I had. It wasn’t all a waste however, since all that work did eventually lead me to something I did want to write about, albeit with not a lot of time left to actually do the writing.
 
The chart below plots the scoring output for how the league-average player performed across each season from S59 to S68. It may not look like there is a huge difference (in hindsight, comparing the “league-average player” across seasons may not be the best way to view what I want to show), but the effects become more obvious when looking at some of the stat leaders; S62 was by far the craziest.

 
 
 
I was already somewhat aware about the high scoring S60-S63 seasons, just from chatter around the forum, but what I never really understood was what caused it. Why did scoring come back down to earth in S64 and plateau? What caused it to go up in the first place? Naively I just assumed that the SHTS sim has lots of knobs that can be tweaked and that some knob was probably turned in such a way that made scoring increase. While possible, that answer is highly unsatisfying. More realistically, there is probably something in the data that can explain it. I had a couple of ideas about what types of things I would think would cause a scoring increase in the VHL.
 
For example, maybe the ratio of the number of user-created forwards to the number of defencemen/goalies was more in favour of forwards? If there weren’t a lot of people that chose to make players in goal-preventing positions, then it would make sense to see scoring go up. The chart below plots this information. As you can see, this mix of positions has pretty much been left unchanged across the years. In particular nothing here stands out about S62 that would explain it's league scoring rates.
 
Rather than looking at the quantity of players at each position, we should really be looking at the quality. It still feels like TPA is the most common way that people measure the quality of a player on the forums, so that’s what I used. Below plots the average TPA invested in each player at each position group (forward, defence, or goalie). S62 clearly looks like the outlier here, with low average TPA across all position groups. Most notably it was the worst goalie crop we’ve seen in a while, mostly due to retirement of the elite guys from the year before. But in terms of the gap between TPA in forwards vs TPA in defence vs TPA in goalie, there really doesn’t seem to be a clear explanation. In fact, the TPA gap favours forwards in more in recent years than at any other point in my data, yet league scoring hasn’t returned to it’s past high.
 
 

                                                                  Number of User Players By Position                                                           Average TPA of Players By Position
 
One obvious flaw to the above line of reasoning is that it doesn’t account for where the TPA was spent. There are definitely high TPA forwards that don’t score much (Current Riga forward Anthony Matthews has the highest TPA “cost” per goal scored in all my data). And there are also high TPA defensemen that don’t prevent scoring well (this is harder to measure, obviously, but the most notable high TPA, high minus player from my data was Luc-Pierre Lespineau-Lebrunette. What a fighter). To account for this without getting too complicated we might only want to look at two TPE stat categories; SC as the strongest predictor of goal scoring and DF as the (most-likely) strongest predictor of suppressing goal-scoring. Below the chart plots the average rating of SC vs DF across seasons. One thing this shows is how investment into DF changed after S63. Despite SC increasing on pace with DF in recent years, it seems that just the presence of DF being invested in played a role in moving past the high scoring early 60’s.
 

 
In my mind, there’s still a lot left unexplained in regards to league-wide scoring variations across seasons in the sim. What I am more confident in saying is that the early S60’s were likely an anomaly; a period where the average player was worse than we’ve seen in a long time, where people didn’t care as much about DF as we currently do, and where the few good players that did exist were able to take advantage and score at an insane rate. I doubt we see something quite like it again.

State of the Nation: IRELAND
As of 10/14/2019
State of the Nation is a series in which I look over active and free agent players in the VHL by their nation. By doing this, I hope to make it easier for those who don't want to go through each player, clicking dozens of times to go from one screen to the next when they can just read about them here. Plus, it gives me something to do while I sit at home and contemplate whether or not cats and dogs understand each other.
____________________________________________________________________________________________________________________________________________________________________________
Over the past eight seasons, there have been eight Irish players to come onto the VHL scene. Some have seen no playing time in the VHLM, while others have solidified themselves as award-winning impact players in the VHL. Perhaps a couple will become Hall of Famers, but only time will tell. As explained above, I will now go on to tell you a bit about each player in order of newest to oldest based on their VHL draft year. As well as asking a question that they should answer in the next few seasons as they continue to develop their players, or not.
 
Odin Omdahl | D | S70 | 43 TPE
VHLM 0 GP | 0 G | 0 A | 0 P | +/- 0
zepheter
Having just created his player, Omdahl is the newest Irish player in the VHL. Sitting at just 43 TPE, he has a long way to go to fill in the shoes of some of the current VHLers who are soon to retire after great careers. Will he be able to meet the expectations set by other Irish players in the VHL? 
 
Kevin Reegsman | D | S69 | 81 TPE
VHLM 49 GP | 2 G | 2 A | 4 P | +7
Reegs
Having played a little under one full season, Reegsman has plenty of time to grow into a respectable presence in the VHL. Expecting a good offseason, Reegsman should be in for a strong showing next season in the VHLM. After a compelling first couple of weeks, Reegsman seems to have slowed down. Will he be able to pick up the pace as Ohmdahl looks to overtake him in the near future? 
 
Owen Nolan | RW | S68 | 335 TPE
VHL 63 GP | 8 G | 6 A | 10 P | -12
studentized
At a pace of about 22 TPE per week, Nolan is on track to hit 400 TPE in about three weeks if he can keep up this tremendous growth rate. Despite the point total of the VHL rookie, he's projecting to be one of the most highly rated players from Ireland. While it's too early to make assumptions on what he could accomplish, there's not much to be desired when it comes to his commitment to get better. Will Nolan be able to keep up his terrific progression and become a star in the VHL? 
 
Colum ODowd | LW | S65 | 32 TPE
VHLM 0 GP | 0 G | 0 A | 0 P | +/- 0
sour_chin_music
Unfortunately, ODowd was never able to carve out a spot in the VHLM. After posting a single update in January of this year, he hasn't been active. Any chance of a comeback seems to be written off at this point. 
 
Denver Wolfe | RW | S65 | 679 TPE
VHL 210 GP | 25 G | 95 A | 120 P | -14
VHL PO 35 GP | 7 G | 14 A | 21 P | -1
InstantRockstar
After being drafted, Wolfe spent S65 in the VHLM, where he racked up 98 assists and 116 points to win the Vladimir Boomchenko Trophy as the player who led the VHLM in assists during the regular season. He also helped lead his team to win the Founder's Cup, earning first star in the cup clinching game. Since then, Wolfe has done all but prove that he isn't worth the 9th overall selection. After posting 36 and 37 points in his first two seasons in the VHL, Wolfe has taken a larger step and thus far has 47 points in 66 games. Just three seasons in, Wolfe is proving to be exactly what his team wanted when they drafted him. Where will he stand in the all-time rankings as an Irish forward? 
 
JB Rift | G | S64 | 732 TPE
VHL  198 GP | 107 W | 64 L | 27 OTL | 0.915 SV% | 2.74 GAA
VHL PO 6 GP |  1 W | 3 L | 1 OTL | 0.880 SV% | 3.90 GAA
Devise
Rift has maintained a high level of play ever since he stepped into the VHL. He started off in a big way by winning the Benoit Devereux Trophy in VHLM S64 as the top goalie in the VHLM. To no surprise, he has continued to keep his team in contention since his first VHL start in S65. Despite not having the greatest run in S66, Rift went on to have a monster season by establishing a record of 47-10-7 with a 0.927 SV%. He stacked up the trophies in the regular season by winning the Aidan Shaw(best VHL goalie), Dustin Funk(most improved), and Greg Clegane(lowest GAA- 2.3) trophies. Sadly, Toronto would go on to lose in five games to the Vancouver Wolves in the NA Conference Finals. This season appears to be in the books for Rift and the Toronto Legion, but will they be able to rise to the occasion with Rift having only three more seasons of eligibility? 
 
Konstantin Mulligan | D | S62 | 751 TPE
VHL  500 GP | 71 G | 167 A | 238 P | +5
VHL PO 19 GP | 4 G | 10 A | 14 P | +5
Pandar
While Mulligan appears to be on the decline, that does not discount what he has done throughout nearly seven full seasons in the VHL. Even though he may not get the most points, he is a wrecking ball at 6'8" 280 lbs. He has managed to register just over 1,375 hits up until now. With six more games left in this season, he could very well reach 1,400 hits and perhaps even 1,500 by the end of next season if he decides not to retire early. Mulligan is a classic case of a playoff performer. He has averaged about 0.48 points per game during the regular season, but averaged 0.7 in the playoffs as a defenseman. As a result of his performance, he made it to the S66 Continental Cup Final with Vancouver. They were bitterly swept by the dominant Helsinki Titans. In S67 he moved to HC Davos, who failed to make the playoffs, while Vancouver went on to win the Continental Cup. Mulligan is back with Vancouver, but their playoff chances are becoming ever so slim. Will Mulligan end his career a champion? 
 
Lando Baxter | D | S62 | 646 TPE
VHL  426 GP | 63 G | 246 A | 309 P | -48
VHL PO 17 GP | 3 G | 8 A | 11 P | +2
Elhandon
Baxter started off his minor league career in Ottawa, where he won the Founder's Cup in S62. Since then, he has served his teams well as a productive offensive defenseman over the past six seasons. Even though he isn't putting up stellar numbers, he will likely go down as the highest scoring Irish defenseman in recent history. It's sad to say, after a long career, he will likely retire having not won any trophies or championships. It doesn't look like Toronto will make the playoffs this season, and he only has one more opportunity to win if he chooses not to retire after this season. However, he is still serviceable and a contender could look to pick him up and help him finally win it all. 
____________________________________________________________________________________________________________________________________________________________________________
DISCLAIMER 
Due to the limitation of only being able to see players from S61 to present in the player list, I won't be able to search for most older players. While that is a bummer, it doesn't stop me from keeping you informed about the most recent VHL players.
If you happen to have any information that I could use to better inform my audience, please don't hesitate to message me.
Thanks for reading, and if you would like to see more of the "State of the Nation" series, just let me know!
 
1,346 words, using for 10/20 & 11/17

Aleksander Rodriguez
Position: RW
Age: 16
Height: 6'5"
Weight: 205 lb. 
TPA: 511
VHL Draft: 3rd round, 24th OA to Riga
VHLM Draft: 1st round, 2nd OA to Mexico City
 

 
Tonight on the Roger Pennies Show™, we fulfill a popular request, reviewing the top scoring forward on the New York Americans, the American born Aleksander Rodriguez. Let's begin.
 
During Rodriguez's two year stint in the VHLM, he skated for two different teams. In S65, his first VHLM season, he skated for Ottawa scoring 24 goals and assisting on 35 for 59 points in his first 72 games. In his second year, drafted to Mexico, he played much better ending the year with 36 goals and assisting on 54 for 90 pts. The team made it to the postseason, but fell short, as did his performance, with 3 pts in 5 games. He managed a 1.03 PPG average during his VHLM years.
 
By the time he had reached the skill to become a VHL player, he had already been traded to New York in a package deal with picks that would become Derek England and Ryo Yamazuki II, as a part of the New York American's plot that offseason to tear their team down and rebuild. In his first VHL year, he managed to score 17 goals and assist on 25 for 42 pts in 72 games for the Americans. In the following (and current) year, he has managed to score 17 goals and assist on 36 for 53 pts, in 58 games. So far in his VHL career he has managed a 0.73 PPG average, managing to be 30th of 153 for total points, and 2nd on his team behind defenseman Guy Legrand, a player who is a year less experienced than him. Additionally, he is 4th in New York goalscoring, two players also a year less experienced than him, one himself who is a rookie, and one who is a second-year VHL player alike Rodriguez. He is certainly not a goalscorer, and one may wonder whether the goalscoring stats on his team are a product of him, or if his playmaking stats are a product of them. There are other questions to be asked aswell, such as the availability of icetime to this player, or if the quality of his opponents has spiked his production, which it may have done to many North American Conference teams. Not to discredit Rodriuguez, however, as he is indeed 30th in league scoring.
 
Six players from S66 or S67 have more points than him, and twelve in total have more points than him OR five less points than him. This makes him right in the middle of the active S66/S67 pack which altogether isn't all that special. Fan's of the league may look more towards his out-competing S66 Counterparts, or his S67 colleagues who are catching up to, or have outright surpassed him with a year less experience, but it also seems like he will be a reliable middle 6 forward who will never require the top paycheck the likes of his teammate Legrand may ask for one day. This means he can still be a valuable asset, or trade bait, though for which team he will play for next year is up in the air, as Rodriguez has stated, rather emotionally, his intentions to not return to New York and hit the free agency market.
 
This has been Roger Pennies, later tonight we'll bring you the full shakedown on the VHLM Dispersal Draft. Again. 
 
 
569 Words. Claiming my 2nd donation doubles week seen here
 

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

Last season Nolan's name did not get picked and it bugged him. "I thought I was putting together a strong VHLM season and was hoping to squeak onto the World team roster. Was weird seeing all my teammates go and not me". 
 
This year is different; the right winger will represent team Europe. Despite having a lackluster rookie season with New York, Nolan will be counted on to score some important goals and log some top minutes in this year's tournament. "I'm hoping to redeem myself a bit since the VHL has been tough for me. The World Juniors should give me a chance to play with a score-first mentality again, something I've went away from this year playing in the bottom six in NY" said Nolan.
 
Some familiar faces will also be joining Team Europe. New York teammates Eller, Elmebeck and Gunnarason will all represent alongside Nolan. "I think it just speaks to how bright our future is in NY. We have a lot of young talent and we all really want to show just how good we are on an international stage". 
 
When asked about his hopes for this years tourney, Nolan responded "Bringing home a gold medal will make this entire year, and all the hard work that went into it, worth it. Anything short of gold would just add more disappointment". Look for Nolan to have a strong tournament.

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

Although the STHS index has fields to track take aways and give aways, the version of STHS we sim on does not actually fill anything in. Still, as pointed out by my teammates on New York, the full play by play game logs contain enough information to reasonably calculate these. So that's what I did. I will briefly outline how I went from full play by play to numeric stats but full disclaimer: it's probably not perfect. I can even post the source code if people are interested in building on it (otherwise I will probably just keep generating these on demand for my teammates when they want it).
 
How I got the results:
 
The game files are all served as html so that's what I parsed from. For any one wanting to try, it wasn't too painful thanks to some handy packages in nodejs. From the full play-by-play, only two sentence "templates" stood out to me as describing a give away/take away. I wouldn't be surprised if I missed some though, so let me know if you know of any others. They are:
 
"Pass by Player A intercepted by Player B (in the neutral/teamX/teamY zone)" and "Player A is hit by Player B and loses puck"  
The first one is pretty cut and dry. Player A gets credited with a give away and Player B gets a take away. I interpreted the second sentence type in pretty much the same way (i.e Player A with give away and Player B with take away). In reality though, the second case could have Player A lose the puck, but in the following sentence have the "Puck retrieved by a Player C" where Player C is on the same team. In this case, I'm not sure that a give away is truly appropriate, but I was feeling lazy so I didn't go to this level. If it makes you feel better, the first type (intercepted passes) is much more common that the latter, so I think the results would be pretty close.
 
One thing I noticed right off the bat is that give aways and take aways are being reported MUCH higher than NHL levels (like an order of 10x more). Perhaps this is the reason that SHTS is not tracking them in our version of the sim. Or perhaps I just interpreted everything too aggressively. Thankfully, I think most people are concerned with their TA to GA ratio rather than the absolute numbers anyways, so I'm still thinking there is some usefulness here.
 
The results (for season 68 up to game 251):
 
Most take aways: Piotr Jerwa @majesiu - 1014
(runner up Sven Hitz @JayF - 1011)
 
Most give aways: Mikko Aaltonen @GRZ - 959
(runner up Guy LeGrande @Steve - 948)
 
Most take aways in a game: Brady Strokpo Jr @Bushito- 41 take aways in game 241
 
Most give aways in a game: Lance Flowers @CowboyinAmerica - 39 give aways in game 190
 
Best take away to give away ratio:  Mikka Pajari @Devise - 177 TA to 94 GA = 1.88 TA:GA
Best take away to give away ratio (min 800 GA + TA): Basaraba Moose @Toasty - 636 TA to 377 GA = 1.68 TA:GA
 
Worst take away to give away ratio: Chico Smeb @xDParK - 193 TA to 425 GA = 0.45 TA:GA
Worst take away to give away ratio (min 800 GA + TA): Mikko Aaltonen - 553 TA to 959 GA = 0.58 TA:GA
 
Next steps:
 
Besides just cleaning up the code a bit, one interesting next step (and possibly an upcoming VHL.com article) is to try to associate each player in each game to their team at that time. This will let me easily figure out which teams are the best in regards to TA/GA as well as make it easier to generate all the player results for a specific team. I could also take this a step further and scrape the line combos and assess performance of lines using TA/GA. An easier next step would be to generate this report for the VHLM since they need some love too. As always, let me know if you have any ideas of other things you'd like to see.
 
Thanks for reading!

o/

Not sure I was fully prepared for the huge changes that take place during the jump from VHLM to VHL. It was a wild off-season and it moved fast. 
 
There is a bit of a bitter taste left in my mouth from getting ousted from the VHLM playoffs with Halifax, mainly around the fact that we don't get to try again. The minors are done and that team is gone. It was a wild ride, one I enjoyed a ton, but within the next couple of days I knew I would be drafted somewhere else. I was excited to move up to the VHL, in part because I knew there, we would be able to keep the group together to try again when things don't work out in the playoffs.
 
The expansion teams announcement added a different wrinkle. Two more teams meant a couple more available player spots, and I was optimistic I'd end up in a good situation with good playing time. My thoughts towards playing for an expansion team were always positive; it would be cool to be part of a team's founding history. But at the end of the day, I couldn't be more satisfied where I ended up. New York has a core of young players that is unmatched in my opinion. Everyone in the room genuinely believes we'll be one of the top teams in just a few short seasons. To be a part of that is humbling and I can't wait to see how our development grows throughout this season.

Boris the forest is the captain @studentized lol, but it's true that D.Wilcox is getting a lot of pts and that some of them were with us. I just feel like Boris the forest will found a way yet again to get a lot of pts mid season

I don't have a player in the league at the moment (he's coming at the deadline) but I'd be interested in the position of updater and taking up the role prior to my player's creation. 
 
When I was in charge of the LIVE Player Rankings as Recruitment Crew, I would go through the update queue prior to updating those rankings and put through everyone's update free of charge, often on a daily or every other day basis, so I have experience with the job (and actually still have the powers leftover from that period). I'm very alert to what is and isn't considered valid in terms of TPE claimed (No complaints were ever levied my way in regards to incorrect updates) and in the past have spotted errors that other updaters had not. 

S67 Commissioners Cup 

 
"What is this?", you're likely asking. Over the last season, @diamond_ace and myself have spent quite a lot of time looking at and talking about how we can improve the VHLM. During our tenure as commissioners, that's always been our goal. Making the VHLM move enjoyable, more meaningful to a player and more efficient with key things like retention and development. This past season, we talked about a lot of things structurally/rule wise that are either ready to be announced or in the final stages of being what we believe to be a system we're confident in. One of those changes is going to be the Top Prospects Game. Last season, it was the first one in a very, very long time. I'm sure it was a thing once upon a time, but I can assure you it's never been this. S66, we quickly put together a roster that we felt was going to be fairly even and extremely competitive. They only played one game. It's a Top Prospect GAME. 12 F, 6 D, 2 G. It was your normal all-star/prospects game. So, whats the change? 
 
Firstly, the name. We present to you the Commissioners Cup. This event will be held during the off-season - every off-season. The event will be the ultimate battle between the two who hold the most power in the VHLM - Team Diamond VERSUS Team Bana. Unlike the past where one person quickly threw together a competitive roster for each and the other said "yep", we actually stayed up late last night doing a 40 pick draft - drafting some of the best the VHLM currently has to offer and putting together two extremely solid teams. The roster sizes are the same (we'll be using the last file of the regular season for attributes etc), but the amount of games played will be different. Instead of 1, you're going to see 3 games. ITS A BEST OF 3 SHOWDOWN. A fight. A BATTLE.... to the death... ULTIMATE GLORY... K, maybe not death - but it's ultimate shit. The battle and intensity is real. The rivalry is real. 
 
WHO WILL REIGN SUPREME? LET THE BATTLES BEGIN!?

With that being said, I guess it's time to release the draft results of the draft, as well as the rosters for the very first ever "Commissioners Cup". Awesome effort this season one again bye everyone involved in the VHLM. This season has been extremely busy for myself and DA, but also a very good one. Thanks!
 
S67 Commissioners Cup Rosters:
 
 
S67 Commissioners Cup Draft:

I got a guy ranked top 8 at like 20th and I need to fix what on defense with one guy at 800 and one at 750ish and 3 at 3-400. Also what I got in my trades isn’t accounted for yet because I have future firsts and also trade up? Nobody was trading a top 2 picks

With S67 now all wrapped up, we can add another data point for cross-season stat comparisons. For this week, I thought I'd look at the TPA earned by players over time.
 
A couple of limitations up front (as a result of my data set):
I'm only looking at TPA, not TPE. TPE that is banked is omitted from this, so in general TPE earning is probably a bit higher than what I report. Whether this banking of TPE is increasing or decreasing, I could not track. This is most notable for players making the jump from the VHLM to the VHL, because of the 200 TPA cap in the VHLM forces everyone to bank after that milestone is reached. Because this makes a noticeable impact, I will split out the results into three sections: VHL growth, VHLM growth, and growth in both leagues. Depreciation exists (and is annoying to measure) so for some players TPA growth does not fully represent TPA earnings. I was about to get accurate and calculate what stats were depreciated based on player age, but then I remembered there are store purchases to counter it, so it wouldn't have been accurate. Ultimately in order to capture this I would need to collect better data sets and track player data at various points throughout season instead of just end of season TPA. Possible to do in the future.  There are two main explanations for why TPA might be increasing (decreasing) over time. 
a) players are more (or less) active and everyone is doing more (or less) work on point tasks, etc.
b) opportunities for TPE earning are becoming more (or less) available
The general feeling I've picked up from the forums is that TPE inflation (i.e more point b) is a thing, but I don't really see a way to measure this aside from looking back through old forum pages to compare. My own personal feelings on the matter say that giving out easier TPE might even be (dare I say it) good for the league. My rationale being that as the league grows, relatively fewer members will claim the "top" amount of TPE possible, and more often than not claim a below average amount from what the average was last season. This is just diminishing returns on recruitment. Obviously  there will be exceptions to this and you will recruit new members that buy in right away and build fast, but for the most part the more committed users will already be here/some other sim league they prefer. More/easier TPE earning opportunities would be a way to counter that and maintain a consistent amount of player growth that we're used to. Those are all just thoughts, however so now let's actually look at the data.  
I looked at end of season (regular season, not playoffs) TPA across players in the VHL and VHLM. Changes in TPA are attributed to the last season i.e S67 data is the change from end of season 66 to end of season 67 and would account for all growth in the following playoffs, the offseason, and the next regular season. It shouldn't really matter where we cut off as long as we're consistent, so I just went with what my dataset had.
 
The boxplots below chart the changes in TPA across season. I have charted the mean TPA change as the red dot and the median is the bold horizontal black line. I looked at three different data sets; VHL growth (players who went from the VHL to the VHL), VHLM growth (players who went from having no data or being in the VHLM to the VHLM), and "all" growth (from nothing, VHLM, or VHL to VHLM or VHL). This "all" growth is the only one to capture the players coming out of the VHLM and into the VHL, so it includes a bunch of higher TPA growth data points due to limitation number 1 (/the 200 TPA cap in the VHLM).
All Growth
 

 
VHL Growth

 
VHLM Growth

 
There are some commonalities in all three data sets, most noticeably that S65 seems to be the most "inflated" TPA growth season of the ones looked at. I was not a member of the forums at the time so not sure really why that would be the case, but I would imagine that either some favourable TPA hand-out existed or that it was just an abnormally successful recruitment season. If we look only at the bottom lines of the boxplots (the 25th percentiles) of the "all" growth chart, we can see that S67 was the first season since S64 where that value was 0 TPA. This means roughly 25% of players who played games in the VHL/VHLM were completely inactive or just suffered from some major depreciation they couldn't recover from. This isn't overly worrying in my mind, especially since the median/average values are staying relatively on pace with the S66 ones, but it does potentially hint at some recruitment diminishing returns. Another explanation could just be that GMs had an increased likelihood to play inactive players this season. Definitely could be tracked over a longer period of time to reach better conclusions.
 
In case anyone is wondering, I can share who some of the biggest outliers were.
 The biggest VHL-to-VHL TPA growth season was for Matt Thompson in S63 with a gain of 255 TPA (from 490 to 745) The biggest season growth altogether was for Hunter Hearst Helmsley in S66 (200 TPA in VHLM to 531 TPA in VHL) There have been 14 players in the VHLM who made it to the max 200 TPA growth all within their first season, most recently being two S68 draft eligible players in Luciano Valentino and Jeff Downey The honour of biggest loser goes to Aleksei Federov in S60 (890 in S59 to 637 in S60, a drop of 253 TPA). Surprisingly his stats were better following depreciation The best S67 VHL growth player was Tyler Barabash Jr with a TPA growth of 248.   
That's about all I wanted to do with this data at this point, but am always open to suggestions to do more if anyone has any. I'm also happily getting married tomorrow and honeymooning soon after that, so this will probably be my last post of this level of quality for the next month. (please still draft me GMs ?)
 
Thanks for reading!
 
 
 
1045 words, so will be claiming for next 2 weeks.