Gustav

Moon Moth's hits/PIM ratio is insane and that plus the goals lead got my vote. Lots of good choices here though.

LAZLO HOLMES CUP LET'S GO

WELL WELL WELL You make a post about how the league is rigging the holiday prize draws and look at just WHAT happens

ONE MORE WIN AND I HAVE MY FIRST CUP SIMON DON’T FAIL ME NOW

That’s exactly what it is and people are just really bad at understanding how parity works. Why it could never swing my way as a GM, I don’t exactly know.

LET'S GO. Literally any outcome other than being swept in the finals (it's happened twice) would be the most successful season by any team I've played on. Lazlo has a championship in the M. Can he take one in both leagues? Tune in and find out.

I like to think I own it well at this point.

If wildcard teams have an unfair advantage, then why did I never win a Cup with Davos? Curious.

TBF I think it paints the picture more clearly/is more explainable to have actual results of a simulation to talk about. It’s also more fun for me to say I simmed each matchup for 175ish series than to just calculate the probability directly; we are a sim league after all. This is why engineering > math. Something that would have been even simpler to do would be to stay on Discord a little longer, because @LucyXpher talked about how Malmo’s morale was higher than Riga’s in this past series—so the idea that it’s a matter of some weird wild-card winner morale boost is completely false to begin with. Regardless of how you do it, something I think is interesting is that regular-season points seem to be a very good indicator of playoff success with my average outcomes matching Victor’s historical numbers pretty much exactly. With that being the case, I was also surprised to find out that most matchups are something like 60-40 in favor of the #1 team, and even the most mismatched ones are about 70-30. In general, I’d totally agree that people don’t get randomness and think the playoffs should be completely deterministic. I’ve seen far worse examples of “the league needs to change the way it works because I lost,” based on much less, going back to my first season here. It’s never going to cease to exist in the VHL, and it’s always going to be a little bit annoying, which is probably why I felt the need to analyze it before it turned into something bigger and weirder.

I still think of you as being 14 shut up

Yeah I think it’s also pretty telling that my article (unintentionally) cherry-picks one of the more skewed eras of the bunch and the stats still say we shouldn’t panic over it. 39% of wild-card winners going on to beat the next team is actually really interesting because that’s exactly the percentage of 7.8 out of 20 that my model predicts twice. There’s definitely some level of coincidence in that, but I think it really backs up what I did with it.

I am going to ping @Spartan , @Triller, @Pifferfish, @Garsh, and @Banackock right off the bat for being around for this convo. Let's analyze a claim before taking a side on it. Some of us here are no doubt familiar with the world of college football, and many of those here are also familiar with a little something called "the SEC" and some really funny things that happened to the SEC this year. For the great unwashed, the SEC is considered the most dominant conference in college football's recent history. With college football's playoff system determined by rankings, there is usually a controversy related to SEC teams when playoff season rolls around. Some SEC fans believe that their team deserves a bit of extra credit for being in the SEC and playing mostly other SEC teams, who they believe are better as a whole than the others. As some people may argue, a team with a few losses should still be ranked highly and make the playoff field if those losses are to other teams who are so good that one would expect them to happen regardless of how good the losing team is. The overwhelming majority of those who support non-SEC teams feel disrespected by this mindset held by some SEC fans and analysts, feel that it doesn't give their team enough credit, and let their rage be known on social media in advance of actually seeing the games play out and finding out just how good any one conference's teams really are. This year, Alabama missed the playoff--something that seemed very unlikely under its new expanded format--and some people on the network who owns the SEC's TV rights lost their minds. Yes, worse teams than Alabama had made the playoff, but it was because Alabama had simply lost more times and thus was ranked lower. Those people just had to cry about it, and they did, with lots of excuses being made for Alabama and lots of promises that the SEC teams that had made the playoff would demolish the competition. After this point, it then got really funny when just about every SEC team in the playoff got flat-out embarrassed, and it also got really cool for me (consider my biased perspective on the above) when I watched my non-SEC team win the championship. It reminded me a lot of something that I've seen way too much over my time in the VHL, which is "my team lost in the playoffs so now I have to melt down and demand that the structure of the league changes to my benefit"-itis. Now, I'm not saying at all that this is what happened on VHL Discord tonight. All that happened was that a claim was made, and to the best of my efforts to represent it in an unbiased manner, it is as follows: STHS provides a morale boost to winners of wild-card rounds in the playoffs, which matches them up better than they deserve with the higher-seeded team in the next round. Because of this, the wild-card winner beats the higher-seeded team more often than they deserve. I'm told that @Advantage or @CowboyinAmerica or someone has done this analysis in the past, but I'm not immediately familiar with it. Since the wild-card team has an unfair advantage and consistently beats the higher-seeded team, we should re-evaluate how the playoff system works so as to avoid this unfair advantage. This is all well and good. I'd completely agree that if it were the case that the wild-card team were winning upset matchups over top seeds at an unfair rate, then mitigating that would be a proper course of action. But still, a claim was made and I don't see any numbers--and I've spent the past years of my life trying to learn exactly when, where, and how to dispute stuff that other people write. To resolve this, I skip the gym and stay up late tonight so I can get to the bottom of this whole thing. To me, it shouldn't be all that difficult to look at the whole morale part. Just wait until next season rolls around and make sure you pop open the index to find out whether that bump from the first round is really there. If there really isn't any change, then the whole point is moot. And if there is one, is there a way to adjust other teams' morale manually to match? I don't know how simming works. For now, though, let's find out whether it really is true that wild-card teams win too often. I'D LIKE YOU TO HAVE A SPREADSHEET THAT I'M PROUD OF. Start on the first tab, which is an overview of every wild-card winner from the past 10 seasons, compared to the teams they then faced in the next round of the playoffs. On the surface, the argument that these teams win disproportionately seems reasonable. After all, wild-card winners have a winning record of 11-9 against top seeds over this time. Clearly, this isn't what should be considered likely or expected. But is it actually outside the limits of what we would expect, or is this explainable by parity? It's important to note that my model is based on one underlying assumption, and that is that it uses regular-season points as a proxy for overall team ability and the likelihood that a team will win any given game. If you don't agree with this, then you don't agree with any of my analysis. I'm not sure there's a better way to do it, though--it's based on proven history over the regular season, and there's the added bonus that those teams had played each other a handful of times on the way to getting those points. Based on this, S88's Vancouver (with 77 points) would have about a 43% chance of winning any one game against Seattle (102 points). One would obviously expect Seattle to win that playoff series--and they did--but there's a nonzero chance that Vancouver would have pulled the upset. About a 33% chance, actually, according to my estimates. Here's how I did that: I took the single-game winning percentage and generated 1000 random numbers with it. If the number were below 430 (out of 1000), I counted that as a win for Vancouver, and if it were above 430, I counted it as a win for Seattle. That gave me 1000 simply simulated games between those two teams, from which I used a separate list to track totals. Every time one team reached 4 wins before the other, the numbers would reset and it would count as a simulated series win for the team in question. As it turns out, again, based on this method, Vancouver would pull off the upset about a third of the time. This is a very simplified example, but it's nonetheless an example of something called Monte Carlo simulation, where models are drawn up based on random generation. I did this for every wild-card winner's matchup with the top seed of their conference, digging up simulated probabilities that were pretty low for matchups like S90's London versus Davos (unsurprisingly won by Davos) and pretty high for the same season's Vancouver versus Toronto. That one, as of right now (all the random generation regenerates every time something changes with the sheet), says that underdog Vancouver actually wins more of the time than Toronto--which I'm OK with. Random simulation is random and doesn't always match what we would expect; as long as it's in reason (and the difference is only slight in this case), that's OK. Obviously, in almost every run, the top seed wins more simulated series than the wild-card team, so we would expect the average number of series won by those top seeds to be greater. I can change numbers on the spreadsheet, but the cumulative win total of all wild-card teams hovers pretty closely around 7.8. That doesn't change much at all--according to my model, if the VHL playoffs somehow managed to have the same sort of huge sample size that my model does, wild-card teams would have a record of about 8-12 over the past 10 seasons. We knew this already, but they have been winning more than expected. What we don't know is whether this actually means anything. So, we dig back into the same random dataset that we used before. Conveniently, we have the probability that a team would have won a series (there's an important distinction between winning a game versus stringing together 4 wins out of potentially 7, of course, but we've already accounted for this). Taking S91's DC versus LA as an example, my model says that DC would have won this series 57 times and LA would have won it 116 times over the 1000 games that I simulated. So, all we have to do is to generate a random number between 1 and 173 (that's just 57+116), and if that number is above 57, it's a win for LA, and if it's below, it's a win for DC. We do this for every playoff series. Thankfully, the sheet changes all its random cells every time I do anything with it, so every entry is a new simulation of every single playoff series. Barring the time it takes for Google to come up with 20,000+ random numbers at a time, this is great--all I had to do at this point was to write down the total number of wins by wild-card teams, wait a handful of seconds, and have my next number ready to go. I simulated 21 different outcomes (to give 20 statistical degrees of freedom), and this gave me a big range of success (and lack thereof) for wild-card teams. Over the course of these runs, wild-card teams put up a record as bad as 4-16 and as good as 12-8 (better than actual history!) in my simulations. 11 wins was even matched twice. Something that makes me feel really good about the accuracy of these runs was that the average of series wins here was also 7.8--pretty much exactly the same as the number I reach when I add the cumulative winning percentages. It's another random simulation that matches the first one pretty independently of it, so I really feel confident that it's describing the state of affairs accurately. Based on these outcomes, I now had an average and a standard deviation, which I could finally use to do something you probably did in high school: a simple t-test for probability. For those unfamiliar, the t-test is a statistical method that's used to calculate the probability that a given data set could have been generated by chance. Generally, p-values below 0.05 are considered "statistically significant" and good reason to reject the null hypothesis. In stat-speak, that means: Null hypothesis: wild-card teams do not win playoff series at a disproportionately high rate. Alternate hypothesis: wild-card teams do win playoff series at a disproportionately high rate. If the t-test gives us a number below 0.05, then we can reject the null hypothesis. This does not necessarily mean that we accept the alternate hypothesis (which is important because it's the reason why we do this to begin with)--it just means that we conclude that the statement I've listed as the null hypothesis is untrue. However--taking the average, standard deviation, and sample size, and considering how far 11 wins is from our average of 7.8, we get a p-value of: This means that at the moment, based on the information I've come up with, I cannot in good conscience agree with a claim that wild-card teams are afforded an unfair advantage in the second round of the playoffs. There are things that can change this. Perhaps if I go farther back in time, I end up seeing more wild-card wins that push the historical average farther away from the simulated one. Perhaps I simulate another 20 rounds and get a more comprehensive distribution that makes 11 wins look worse (although I wouldn't count on that based on my averages matching and 11 wins appearing to be well within the range of variability). It is not an incorrect statement that wild-card teams have won more often than expected in recent seasons. But, if it were proposed that we change the playoff format because wild-card teams are finding success, I would not currently support it because I can't reasonably say that they're finding it unfairly. I had no idea what the outcome of this analysis would be when I started it, and if I'd come across more significant results, I would have gotten fully behind that idea. So, with apologies to the Moscows and the Malmos and the Vancouvers of the world, sometimes these things happen and that's OK. I hope it can at least be respected, whether or not you agree with me, that I make my case only after having done my best to back it up.

Off the top of my head, Calgary has a cool tie to their team concept. "LA Stars" also works well as a nod to the film industry. In the M, the Ottawa Lynx share a name with a former minor league baseball team and Miami works really well as a team concept because of an extensive history of piracy around Florida. I'm not immediately aware of other things for other teams though.

Oh shut up. One day I'll have a championship.

Is this new or am I just never in the playoffs?

Some absolute shocker games today. Bears to the next round, and if we can take down Vancouver we can take down anyone. One round at a time and maybe I get that first championship I've been chasing.

Week 3

I’m fine with the “do nothing” strategy for the sake of keeping things competitive for a group of players who cares about the team; it’s serving them well at least. What I don’t like is doing nothing for the sake of not wanting to do anything and then quitting when everyone disappears. Davos coming up on a rebuild was a contributing factor to my leaving, but I made sure there were still sellable assets when I left and left the choice to tank up to someone new. I think it wouldn’t have been very nice to leave behind literally nothing to work with, and that seems to be getting more common in recent seasons. I do wonder whether thinner draft classes are locking teams in to the bottom of the league—if you can really only get 1-ish quality player per draft, it’s going to take so long to build up a roster that you probably won’t be able to retain those initial assets long enough. Case in point, I never asked to be moved out of Prague but that was most of the reason why it happened. So I think our next big drive (whenever that is) could really start to shake things up again.

Highest-ranked #2 in a group story of my life

I made this years ago

Love the name and welcome back!

You're super far from the first (and the last) time this has ever happened--situations come up all the time that very understandably remove people from the VHL. The fact that I mentioned you in this article and you're still active enough to read it says a lot.

Maybe this is why Devise sold all his picks--what was Toronto thinking? We'll break it all down here. Why I do this, I don't really know. This article started as an analysis of draft classes over time, where I set out to track draft busts across each season and see if we've had any trends worth thinking about. I found out pretty quickly that the results of that analysis were inconclusive, but at that point I had a spreadsheet with lots and lots of data to look at. I wasn't going to waste that, so I thought up something different. With the S89 class in their last season in the VHL, we now have a clear evaluation of which picks have and have not worked out over the whole S80s. So with my data on draft busts, why not write an article about those draft busts? It's going to be a long one, but I promise that it will be worth your time. I don't want to formalize this as the official start of a series, but I've done things this way in the past and I think it's fun, so I think I get to draw special attention to the heading: Let's Invent a Stat! It's surprisingly easy to crunch the numbers when you get to decide what the numbers are in the first place. What I'll be doing here is creating a number that I'm non-creatively calling Bust Grade (BG) to rank exactly how much of a draft bust each draft bust from the S80s is. Our equation for this is: BG = (DAD + GA + PA - DAL + (EXP/10) + ((1-(LEN/8))/10))/1.07 Of course, these are unfamiliar abbreviations, because I've also made them up: Draft Adjustment (DAD) = -0.2*Diff/12, where Diff = "Differential," or the difference between a player's draft position and their final TPE ranking within their class. The largest DAD from the S80s is -12, and we multiply this by -0.2 to make the number positive and also to limit its efficacy. So, a player whose final TPE ranking matches their draft position will have no points applied to their BG, while the player who dropped 10 spots down the list will live with 0.2 extra. Gain Adjustment (GA) = 0.6*(1-(Gain/1000)), where Gain is the amount of TPE earned after the draft. This number gets larger as that amount gets smaller, making a player more of a bust with less TPE earned. No player on this list earned over 1000 TPE after they were drafted (of course, because how would they be a bust in that case?), so every player has some contribution to their final BG total from GA, which increases to a maximum of 0.6 points. Pick Adjustment (PA) = 0.3*(1-(Pos/16)), where Pos = Draft Position. A player drafted 16th overall, at the end of the 1st, gets no extra contribution from this number, while a player drafted 1st overall would receive its maximum of 0.3 points. This is because a player who is drafted higher (and with larger expectations) should be considered more of a bust if they don't work out. Draft Allowance (DAL) = -0.4*(1-1/(1-0.12*(NUM/5)+0.08*(BP/40)))), where: NUM = the number of busts present in that draft, and: BP = "Bust Points," a system I came up with to assign points to a draft to gauge the chances of making a bad pick. Whether a player is a bust or not, they earn Bust Points for their draft through recording final TPE totals, Diff, and Gain that are below certain totals. Either 1 or 2 points can be added in each category, depending on severity, and most players who earned at least 4 Bust Points are present on this list. DAL, unlike the others, is a forgiving number. A player who is present in a draft with lots of busts (or disappointing player numbers in general) will have more DAL subtracted from their BG than a player who was a bust in a draft where just about all picks around them went great--essentially, a mistake made in a good draft is a larger mistake. Up to 0.2 can be subtracted from BG by DAL; with up to 0.12 coming from NUM and 0.08 from BP. EXP = "Experience Factor," which is just a flat 1 if the player was a recreate and a 0 if the player is a first-gen. This is divided by 10 to make it a difference of 0.1. A bust is a bust, but it's more of one if the builder has a reputation that would give more reason to believe that they would turn into something. LEN = Career Length. I wasn't going to factor stats into this quantitatively because it would just be too much to compare forwards to defenders (and especially to goalers), but we can at least use the length of a player's VHL career to give us a small correcting factor for how much they contributed to the VHL. A player who played for 8 seasons earns no extra points from LEN, while a player who never made it up registers an extra 0.1. This is all divided by 1.07. I tried to make a high total approximately equal to 1, and it turned out that the highest grade was a 1.07. So, we divide all numbers by 1.07 to make a 1.00 the highest BG possible and give us a nice round number. We've invented a stat! I'll note a few things: Who is and isn't a bust was aided by my analysis of Bust Points but ultimately up to my own opinion--my rule of thumb was to include anyone with at least 4, but I made a few exceptions. A prime example of this was @Pifferfish's Elias Lampi, who put up 4 Bust Points by recording a moderate career TPE total and dropping 8 spots in the TPE rankings in a loaded S86 draft. My spreadsheet says he's a bust (and he has a higher BG than some others in this article), but he was a solid player stats-wise and even won the Labatte in S91. Numbers aren't perfect. On the other hand, I threw out players who didn't work out if there was really no one good drafted after them. S87 and S88, for example, have no busts at all because they were very thin classes--those drafted early stuck around because they were expected to, and those drafted late were never supposed to be good in the first place. I'm not going to call players busts if there were no good options left on the board. I kept this analysis to the first round only. Beyond that point, anyway, picks that don't work out aren't usually huge mistakes. Here's what BG looks like when it's broken down per pick. Perhaps you can guess what some of those picks were! According to my analysis, interestingly, London and Riga only messed up once, but both messed up big by making the worst picks of the decade. We're just about ready to break down the list, but I have to note something else that's important. This isn't just a ranking based on BG, because I've passed all of these players through a different filter called My Personal Opinion. Things like stats mean a lot and aren't reflected in BG, and there's also something to be said for personal history--a recreate known for going inactive shouldn't be considered as much of a bust as one who has shown huge earning potential, and I think about this too. I actually really like BG as a general correlation, and it helped me a lot with sorting out where to put everyone on this list, but you'll notice that I go out of order a lot. In total, it's just a list of opinions, but I think they're more legitimate if I've tried to work them out with numbers. On to our ranking of the draft busts, in reverse order: 22. Robert Wilk | 12th Overall, S81 | RW | 210 TPE | @Tomat0 BG: 0.40 Wilk showed lots of promise with an 84-point rookie season, but getting passed around to six different teams over a 7-year career is something special. It turned out that his rookie season would be the strongest in just about every category, and the decline that followed saw him failing to hit even 30 points in a final S88 campaign with the Stars. The S81 draft was a risky one to pick in, especially for the early part of the decade, but this was one of the picks that didn't work out. 21. Justin Adolfsen | 14th Overall, S83 | LW | 205 TPE | @NJDevils24 BG: 0.38 The S83 draft wasn't bad, but it was interesting in that its first two picks (Scotty Sundin and Brandt Fuhr, neither one a bust) earned significantly less TPE than many players who followed. In fact, Landon Wolanin (who would lead the class in TPE) was drafted just one spot ahead of Adolfsen. Though Adolfsen didn't go inactive immediately and got close to 700 TPE, he only broke 50 points once and recorded a net -70 rating in a 6-season career. Some of the picks that followed would have been much nicer for the Americans: Tomas Sogaard, Brian Kowalski, Hall-of-Famer Jake Thunder, and others. 20. John Richards | 16th Overall, S86 | RW | 242 TPE | @John Cimarno BG: 0.48 I debated including Richards on this list because the S86 draft was very binary--the first round was one of the most stacked we've ever seen, and the field was pretty limited after that point. Still, there were players after Richards who made something more out of their careers, and it's fair to say that Richards was the first and only real disappointment of S86's first round. Only earning 207 TPE after the draft, Richards also only made the VHL for 4 seasons, three of which were with different teams. 19. Tadhg Byrne | 16th Overall, S81 | G | 196 TPE | @teknonym BG: 0.62 Byrne is our first player to never make it to the VHL, and it's with good reason as the Irish goaltender only earned 44 TPE after being drafted. This even looked like a good pick--Byrne's agent was doing 6-point tasks and max earning before disappearing over the course of a couple weeks. Interestingly, Byrne kicked around the E for all nine seasons after the draft, sometimes starting, sometimes as a backup, always as an inactive. 18. Baxter Arcanum | 8th Overall, S83 | D | 223 TPE | @ctots BG: 0.56 Arcanum is one of just a few players on this list to break 700 TPE over the course of a career, but makes the list after tapering off in activity from a highly active max-earning start. I'll admit that I didn't know off the top of my head who ctots' first player was, but I remember ctots being super promising as a first-gen and I took the liberty of going slightly out of order BG-wise because Arcanum could have been the next big thing in ways that Byrne probably wasn't. Arcanum would go on to play five seasons in the VHL, spent mostly with DC, and manage to win a Cup in S85 before wrapping it up with modest career totals. Other players who hit on their potential after Arcanum's selection include everyone I listed for Adolfsen from the same season, plus Calgary's twin powerhouses in Goncalves and Wolanin. 17. Wumbo | 10th Overall, S82 | G | 276 TPE | @Fire Tortorella BG: 0.59 I started writing out @Fire Tortorella's name as "flyersfan" before realizing that his name hasn't been flyersfan1453 (I think that's the number anyway) in years and that much of the community probably has no idea that that was the case and also probably thinks of someone else entirely as flyersfan. Anyway--the builder who I still think of as flyersfan and the former creator of Hall-of-Famer Smitty Werbenjagermanjensen came back in S82 with fellow SpongeBob reference Wumbo, who wasn't a super top-tier prospect but who was picked up by a team who had found big success in the past with other mid-level goaltenders (remember Ajay Krishna?). There wasn't really a better goaltender available in S82, and Wumbo would in fact pull a Krishna run of his own by winning the Cup in S84. He really wasn't a bad player, but he'd go inactive with under 700 TPE and wasn't the long-term solution that the Predators had hoped for. After being shifted to backup as an inactive in S88, Wumbo returned to starting in a disappointing 10-win campaign in S90. 16. Maxwell Mathias | 15th Overall, S82 | RW | 260 TPE | @Underclass_Hero BG: 0.72 If it weren't for drafting first-gen superstar Girts Galvins in the second round, S82 would have been a horrible draft for Warsaw. Yet another player they valued above Galvins was Mathias, who brought steady TPE from affiliate leagues and seemed like a sound choice as the GM of Oslo and a job-holder in the E. This wouldn't last too long, though, and Mathias topped out at 332 TPE. Making it up for just one season in S86, Mathias put up 23 points split between Davos and Riga before exiting the VHL for good. 15. Tater Tottingham | 6th Overall, S85 | D | 241 TPE | @Trunkxolotl BG: 0.64 I'm not entirely sure what the expectations were for Tottingham, because this player ended up earning just about exactly the same as the agency's previously-represented Tater Tot. Now, that wasn't bad at all--786 TPE is hardly something to be embarrassed about and Tottingham put up some solid seasons. It's just that Malmo put a bit too much stock into the TPE totals on the board and overdrafted by a bit. Picks following Tottingham (who was traded before ever putting on a Nighthawks jersey) include higher earners Alfred Champagne, Sunglasses Joyo, and Nikolas Kauppi, plus the greatest goaler of the generation in Jesse Teno. 14. Reid Johnson | 9th Overall, S85 | D | 232 TPE | @TopTiddee2 BG: 0.65 It's really not on purpose that this is another pair of players picked by the same team in the same season; I'm not too sure but I'd at least assign some of it to identical adjustments to BG being made by coexistence in the same draft. Unlike Tottingham, Johnson also eventually played for Malmo, but this would only happen after being traded back to Malmo from Helsinki in S88. All of the "Malmo could have drafted someone else" complaints still apply, but perhaps Helsinki (having traded for both) suffered a bit more. In any event, Johnson only played 3 VHL seasons and retired with under 600 TPE, receiving a fair ranking as more of a bust than Tottingham despite a lower draft position. 13. Cadmael Ixazaluoh | 5th Overall, S81 | D | 242 TPE | @Vice BG: 0.69 The Legion were not wrong to draft Vice, one of the more promising first-gen prospects in recent memory (this article also makes me realize how "recent" or not this was, because I have a specific memory of talking to Vice on Discord in a place I only would have been 3-plus years ago). Ixazaluoh broke 600 TPE and spent 7 seasons on Toronto's blue line, and in that sense was valuable, but just didn't maintain that super promising first-gen activity for long enough to become a star player. Vice is back and better than ever with Davos' Johnny Tsunami, who could end up being a symbol of what might have been the first time around, but Ixazaluoh will have to settle for being one of S81's five busts. 12. Tyler Busser | 9th Overall, S84 | C | 309 TPE | @diacope BG: 0.75 The to-be-banned user behind Busser had raised some concerns for GMs by S84, but that didn't stop them from accumulating a TPE total and earn rate well worthy of the first round. To be fair, this kept up right up until an abrupt retirement in S85, making this a really weird pick to try to grade and one that's probably logically worth placement higher up on the board. Only playing one season and being at the center of issues that came up during that time says a lot about the magnitude of a bust for a max earner, but part of me also wasn't surprised when this happened and I feel that Toronto should have known better--especially since higher-TPE and perfectly proven noncontroversial players were left on the board (why AK92 slipped to 13th overall remains a mystery). I would have graded this as a reach when it happened, so how much of a bust is it really? In this case, I'm happy to have the numbers to back me up. This is where Busser was ranked per BG, and I have not changed it. 11. Otis Boudreaux Jr | 9th Overall, S89 | LW | 230 TPE | @Ozzy Batty BG: 0.75 Boudreaux was a very good pick and a very promising user as a first-gen, and one who I have lots of nice things to say about. Things just didn't work out for his first player, who looked at first like a solid mid-round pick in a thin S89 draft. Unfortunately, Boudreaux also never made it up to the VHL with the Stars, and only scored 11 goals across four seasons, each with a different team, before retiring for a do-over. It's ultimately more sad to see a player fade away than burn out, and the fact that each of the next four picks proved to be decently active plants him firmly in bust territory. 10. Milan Dvorak | 5th Overall, S82 | D | 283 TPE | @solas BG: 0.73 I love solas, and Dvorak wasn't far removed from the reign of Chicago's franchise goaler Jean Pierre Camus in the S70s. Across an 8-season career, Dvorak wasn't bad defensively but also generally played for mediocre teams and never put up more than 50 points (including a disappointing 0-goal campaign with DC in S89). In a very Aron Nielsen-type career, he occasionally came back for welfare but as a whole was barely active for a bit, just barely cracking 700 TPE and going before a whole host of good players later on in the round. In the same way as players like Tottingham, he wasn't bad, just a bit of a reach--and worse numbers in the metrics that calculate BG push him farther up the list. 9. Montgomery Burns | 3rd Overall, S89 | D | 300 TPE | @LastOneUp BG: 0.79 Burns is closing in on a 400-point career this season, but in much the same way as in the early S70s, his agent left the VHL on short notice after building a player with a solid foundation for success. The S89 draft was terrible in general (and about to be heavily featured here in the top 10 despite corrections made for it being terrible), but very good players were available at #3 and Toronto mostly swung and missed. Even though my numbers (and I like to think my list) account for bad draft classes by being lenient to their players, having a pick at #3 overall is huge in any draft and there were seven players drafted after Burns who have exceeded his TPE total. Burns put up 82 points in S93, but apart from that has called four different places home in an otherwise nondescript career. 8. Eric Queefson | 2nd Overall, S89 | D | 337 TPE | @twists BG: 0.75 A player who broke 800 TPE is probably not the first player who comes to mind when thinking of draft busts and how to rank them, but Queefson probably already deserves it a little bit for his name and a little bit more for having the worst career of any second-round pick of the S80s. He gets some forgiveness from S89 being a horrible draft pool, of course, and it's tough to rank someone with the highest TPE total on this list this high up on it, but he's also the highest-drafted player on it and it means even more than Toronto whiffing on the #3 pick that DC whiffed on #2. Queefson went straight up to the VHL after the draft and would not have made this list had he stayed active, but he retired in S94 after his first round of depreciation and hadn't done too much of note up to that point. Queefson was OK at times on the scoreboard, but his career totals and lack of dominance stand in stark contrast to his draft position. 7. Zyn Westwood | 5th Overall, S89 | D | 272 TPE | @Sullvino BG: 0.84 There's a big difference between Westwood and Queefson in terms of BG here because it heavily weighs TPE earnings, and I'm up in the air over who belongs where. It's true that Queefson had higher expectations, but Westwood certainly failed harder, spending most of his time as a roster filler on rebuilding teams--how does a -85 rating last season sound for an early 1st-rounder? Curiously, Westwood's agent has created just one other player in the VHL (Aston Martin), joining just a season after I did and doing essentially the same thing in earning well until the draft, going in the top 5, and going not much further. Perhaps this is something that could be a red flag to GMs in the future, but the earning potential is there--the VHL of the future could see something great if that's locked in long-term at any point. It just...hasn't been done yet, and Westwood is a good example. 6. Pope Francis | 8th Overall, S89 | D | 297 TPE | @nurx BG: 0.79 I gave this spot to Francis above Westwood for a few reasons. By the numbers, Westwood has a higher BG because he was picked earlier in the draft, but Francis was rated just as highly at the time of the draft and certainly had a more solid reputation as a former M GM and someone who was highly visible around the forum and Discord. Francis came into the draft asking to play only for teams who did not scout him, so perhaps it's fitting that what he gave his team was the return that teams usually get for not scouting. Playing only one season for the Stars, Francis would fill rosters for two more, on two other teams, before an early retirement with exactly 100 points to show for it. 5. Astro Singh | 5th Overall, S85 | D | 273 TPE | @8Ovechkin8 BG: 0.96 Here's the highest BG out of anyone up to this point! Singh has the misfortune of only making it to a career total of 439 TPE in a stacked draft, dropping far down the TPE rankings list. That plus a short career and (technically, after many years on end) being a recreate leads to a uniquely high BG. Perhaps more uniquely, Singh was out of the VHL entirely after just one season after going inactive and being released in S87. Had he not been claimed by Vancouver in S91, his BG would be even higher and he'd probably be the highest-ranked player by number on this list. He's one of the more unique players on this list in general--I had the pleasure of getting to know 8O8 on a basic level over the time he was here and he was really nice, plus it seemed like he was excited to end up with London where I think I was still technically AGM. But playing for five teams over four seasons, in a career that technically spanned eight, and being drafted higher and earning lower than others who have already been discussed here, is a very weird combination of factors and one that's worth significant mention. 4. Harkat Mulds | 9th Overall, S81 | D | 359 TPE | @hylands BG: 0.79 Mulds was second in TPE on draft day and the product of an agent who had put up as strong a showing as is realistically possible from a first-gen career. There was no reason to believe that this should have been as low as a 9th-overall pick (except that I think I remember hearing after the draft that there actually was for some reason that I don't remember, and maybe I'm making that up entirely so we'll just roll with it). In any case, a super-earning prospect that had the TPE total to warrant a top selection only earned 154 TPE after being drafted, finding a new home every season after S82 and never living up to the franchise defender dream held by the Legion. Interestingly, Mulds played for both Moscow and Prague (not terrible teams) on two separate occasions and put up a decent point total, so perhaps #4 is a bit harsh here and I should have trusted the metric a bit more, but I'm a bit too lazy at this point to move and reformat this paragraph. 3. Cobalt Burns | 4th Overall, S84 | D | 351 TPE | @Ledge BG: 0.90 S84 was the class for lots of S75 recreates, and it was supposed to be a big deal because of this--I specifically remember Ledge talking about how loaded up it was going to be in some article. Ledge was also a former M GM and highly active member who was building what seemed like a very safe pick in that recreate class. Curiously, in much the same way that our aforementioned Johnson and Tottingham were traded early on from Malmo to Helsinki, Burns was almost immediately traded from Helsinki to Malmo a season earlier. I don't think either team intended to move draft busts at those times, but somehow none of those picks worked out. Burns was the most extreme example of this, though--he did manage to earn 600 TPE, and put up a surprising number of hits during a stint with Chicago, but being one safe pick out of many doesn't quite work in one's favor when you're also one of the only ones to turn unsafe. Ledge hasn't been back to the league since Burns, and having been in a couple draft classes and shared some of the same experiences with him, he's one of the players on this list that I miss the most. 2. Maximus Decimus Meridius | 4th Overall, S81 | RW | 281 TPE | @Beaviss BG: 0.77 When making this article and thinking about who I would rank where, MDM was one of the only players that immediately came to mind and also managed to be the player who I thought would be #1 off the top of my head. MDM isn't #1 by BG score, and that's pretty understandable--he accumulated almost 700 TPE and was in a draft that had a bunch of shaky picks. That said, being a recreate who was drafted pretty early on gave him a high BG score anyway, and out of everyone on the list, this was always going to be the player who was ranked higher based on vibes. In the past, every single player created by Beav had set records for TPE and at least seriously challenged for the Hall of Fame. Just "being a recreate" isn't enough to quantify the ranking here when 700 TPE used to be a walk in the park, and Beav's withdrawal from the league during MDM's career was as shocking as any. He doesn't quite earn the title of the biggest bust of the S80s, but he easily qualifies as the most high-profile bust, and the vibes make this make a lot more sense than most newer members realize. "But Gustav," you say, "if you thought all along that MDM was the biggest draft bust, then who is it really? What changed your mind?" To that, I ask that you allow me to reveal the true biggest draft bust of the S80s... What do you think? Would you like to see me invent more stats in the future? This article was a lot of work, but it was also lots of fun. If you're curious, you can find my spreadsheet through this link, where I have all the stuff I talked about plus some (disorganized) info on every draft class of the S80s. I hope you enjoyed whichever parts of this you were willing to read, and I'll catch you next time! 4,900+ words/see you in a month