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Hockey Summary Project (December 1, 2003)

Posted 11:32 p.m., December 1, 2003 (#1) - Dackle
  Too bad hockey stats are so primitive. When baseball ends and hockey starts (go Canucks!) it's like going back to kindergarten again. Can you imagine if the only baseball stats were runs, RBI, and what inning they occurred in? But whatever, hockey makes up for it by being such a great game. I like also that the hockey encyclopedias and sites like hockeydb.com carry full minor-league/junior numbers. You just can't go wrong with team names like the Swift Current Broncos, Brandon Wheat Kings, Moose Jaw Warriors, Kamloops Blazers (Western Hockey League), Laval Titan (formerly Quebec Major Junior Hockey League), Dynamo Moscow, Lokomotiv Yaroslavl, Metallurg Novokuznetsk, Elemash Elektrostal (Russia) and Slovan Bratislava (Czech Republic/Slovakia).

Correlation between Baserunning and Basestealing (December 10, 2003)

Posted 4:21 a.m., December 11, 2003 (#19) - Dackle
  Incredible stuff Tango, arguably the best I've seen here or on Fanhome in months. Michael Humphreys, nice to tie it together into an essential number -- QAD-BR, a sweet stat desirable to see on a regular basis next year and beyond. You guys rock.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 11:00 p.m., January 13, 2004 (#2) - Dackle
  The impact could be double though if comparing a guy facing a .536 opponent with another guy facing a .464 opponent. It's a 36-point swing from .500, but 72 points when comparing them with eachother.
For teams last year I found Minnesota gained .043 in w% due to their opponents, and the Mets lost .043. That's a 14-game swing in the standings, which is huge. They were 23-1/2 games apart but the difference was more like 9-1/2 games. Mets had 72 games against the Braves, Marlins, Phils and Expos and another 12 against the Giants and Yankees. That's 84 games against tough teams which Minnesota only played 10 times. Twins had 38 games against the Indians and Tigers, and 31 games against the Devil Rays, Padres, Brewers, Rangers and Orioles. Total -- 69 games against weaklings that the Mets only had the luxury of facing 18 times.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 1:11 p.m., January 14, 2004 (#10) - Dackle
  What if the question you want to answer is not "What is the true value of this player/team's opponents" but rather "How much were this player/team's statistics displaced by the distribution of its opponents within a self-contained season?" In that case I'd use the old method. MGL, how would you calculate the "true value" of a team? Is it done the same way as players? Do you use 5-4-3 weights?

MGL takes on the Neyer challenge (January 13, 2004)

Posted 11:07 p.m., January 14, 2004 (#15) - Dackle
  From post 12 --

"Let's say that a team (or player), team A played against another team, team B, once (let's say) that had an overall w/l of .600 in a 100 game season, and there were 101 teams or something like that. Other than using it to estimate that team's true talent, of what relevance is it to team A what team B's record was against other team's?"

Because you adjust team A and B's strength by the strength of their opponents. You also adjust the strength of team A and B's opponents (the other 100 team in the league, say teams C through Z) by team A and B's strength. Following the game between team A and team B, the strength of team C through Z's opponents (which includes teams A and B) has to be adjusted slightly.
Does it bother anyone about pythag that removing a 16-1 win from an 81-81 team scoring and allowing 787 runs reduces its pythag wins by 1.8? I wonder if a game-by-game calculation would make it more accurate? I'm all for the granular approach, but it doesn't explain that elusive way in which teams turn components into wins. I'm starting to believe that wins explain the flaws in runs scored/allowed, not the other way around.
It would make my day if someone came up with a correct set of weights, both for prior seasons and at any point during the current season. A good vigorous least-squares regression analysis on all players with 20 PAs from 1900 to 2003 would hit the spot nicely. I suppose that "someone" will have to be me, one of these years.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 12:00 p.m., January 15, 2004 (#19) - Dackle
  Pythagorus shouldn't work as a proxy for strength. A team which has a pythag w% of .750 after five games is not a .750 team.

"True talent" is just one way of looking at the question. I'm more interested in how the won-lost records have been displaced by the schedule. If I learn that a .536 team would be a .550 team (using the old method) with a balanced schedule, I'm not assuming that the "true strength" of the .536 team is therefore .550. I'm just recasting the won-lost record of the .536 team, and its leaguemates, in a way that removes the distortion of the schedule. There is nothing wrong with accepting won-lost records at face value. We don't, for example, adjust the games-behind column in the standings for the "true talent" of the teams involved. Many schedule adjustments or power ratings, which use the old method, are really just advanced extensions of the winning percentage and games-behind columns.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 3:22 a.m., January 16, 2004 (#26) - Dackle
  Yes, I'm comfortable doing a SOS 10 games into the season. If Baltimore starts 9-1, it doesn't bother me that their w% is .900, even though they were 71-91 last year, 67-95 in 2001. And so if they'd played seven of those games against the Yankees, three against Boston, and I do schedule adjustments and bump them up to .923, that's fine. I know they aren't a .900 team, or a .923 team. But .900 is an accurate description of their play thus far, and .923 is another description which combines Baltimore's won-lost record and the performance of its opponents. Maybe the root of our differences is this: I want to describe the past (the sample data), you (MGL) want to predict the future (the true value which caused that sample data, and what sample data will likely emerge in the future).

MGL takes on the Neyer challenge (January 13, 2004)

Posted 10:39 p.m., January 16, 2004 (#33) - Dackle
  Tango, if you take Kansas City's 9-1 record at face value, then you have to do the same for its opponents. You can't mix actual records with opponents "true talent," because Kansas City is an "opponent" for 13 other teams. This would result in one set of calculations using KC's 9-1, and another set for the other 13 teams using 6-4 as KC's record. The league is a self-contained interlocking unit, where every team is also an opponent. Because of this, you have to treat teams and opponents the same way. KCs 9-1 record is as much an anomaly as a 7.89 ERA by Pedro, but nevertheless, it is a description of what has actually happened on the field. I see no problem with modifying that descriptive information with a schedule adjustment which relies wholly on other descriptive information.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 11:47 a.m., January 17, 2004 (#35) - Dackle
  I accept MGLs method as an alternative to the old method. It's just not the way I'd like to proceed.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 12:54 a.m., January 18, 2004 (#37) - Dackle
  I want a system where I can take adjusted team records, compute expected wins by each team against every other team, and have the results equal the actual major league standings. I call the difference between the adjusted and actual records a "schedule adjustment." If I make a little dice league and replay the season, each team's chance of winning has to be calculated by working backward from the actual won-lost records. Using regressed values doesn't work, because it just leads to regressed results, when actual results are what I want.
I would also feel very uncomfortable if the league totals didn't add up to .500, which is a possibility if the system isn't self-contained.
Finally, it seems misleading to say "The Royals' 9-1 record was helped 0.8 wins by the schedule," when the 0.8 is calculated using regressed values. Especially when the speaker goes on to argue that this means quality of competition is unimportant, because the adjustment is so small. You should say "The Royals' 6-4 regressed record was helped 0.8 wins by the schedule." But if the listener is more interested in knowing the adjustment in terms of the actual record, then you should say: "The Royals' 9-1 record was helped 2.8 wins by the schedule," where that 2.8 was calculated using actual records.
I admit though that if we travelled back to April, 2003, each team's true value was unchanged, and we played the season again, the standings would come out differently. I have a feeling that it is this "true value" that MGL, AED and Tango are interested in. This is valid and interesting, but it doesn't tell me what I want to know. I want, on average, for the 2003 Red Sox to go 95-67 in my theoretical dice replay. I can do this with the actual records and just one piece of additional information: the schedule. The difference between the actual and adjusted records will then reflect the schedule and nothing else.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 4:45 a.m., January 18, 2004 (#39) - Dackle
  Hmm, I must be on the right track. No criticism from MGL besides familiar bombast.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 11:46 a.m., January 18, 2004 (#41) - Dackle
  And I suppose you want the Royals to want to go 9-1 each time, right?

Exactly. Otherwise it wouldn't be a fair description of what happened.

MGL takes on the Neyer challenge (January 13, 2004)

Posted 9:04 p.m., January 18, 2004 (#49) - Dackle
  Tango's #43 post, I think, explains my perspective very clearly and fairly. AEDs #48 brings up a thorny problem of doing iterations backward from the real records, although I don't think the answer is using priors. Sometimes at really low game levels the iterations don't interlock perfectly, and the culprit is either: (a) every team has not played every other team yet, so we can't judge the strength of the opponents by the opponents' opponents (if that makes any sense); or (b) there are some unbeaten/winless teams. In the case of (b) I would start every team with a .500 record and let the iterator run 10,000 times. The unbeaten teams get up to .998 or thereabouts. But if they're a 14-0 team against really shitty competition (you would know this by the records of the competition's opponents), they won't get nearly as high as .998. It's a flaw, but it's a mathematical problem that could be solved with a more clever approach. I don't think it means that priors are required.
It's hard to know exactly what people want to know when they ask "How much was Minnesota's 90-72 record helped by playing in the Central?" If I say it was 6.97 wins, I agree that might comprise 4.5 wins due to luck and 2.47 wins due to the opponents' true talent (or lack of), and so 2.47 wins should be the correct figure, not 6.97. It depends on your perspective and the degree of sophistication you require. But if you give me 2.47 wins, then I'd like to see 90-72 broken down by luck and skill as well.

FANTASY CENTRAL (February 21, 2004)

Posted 3:08 a.m., February 22, 2004 (#1) - Dackle
  OK, can we have the 2001 to 2003 data, plus the 2004 projections?

FANTASY CENTRAL (February 21, 2004)

Posted 10:24 p.m., February 23, 2004 (#19) - Dackle
  What do you think is a better way to evaluate players: (a) adding their stats to an average team; or (b) comparing them against other players? If you choose option b, you're assuming a perfectly efficient league where the top (# of league teams * roster size) players have been snapped up. This isn't unreasonable. The information for b is easier to find too -- just dump the current stats into Excel and calculate standard deviations. In choice A you've got to go hunting for historical league data.
If you choose option B, then could you not divide the stat by the raw standard deviation? Most people would subtract the mean of the player pool, then divide by the SD to get a Z score. But why not assume that every player starts at .000, 0 HR, 0 RBI, 0 SB? Then you subtract 0 in each stat and divide by the SD. The only flaw I can see would be unfair bias for or against rate stats. Maybe there's no bias though.

FANTASY CENTRAL (February 21, 2004)

Posted 4:14 a.m., February 24, 2004 (#21) - Dackle
  I use the raw numbers for the counting stats, and (bat. avg. - league bat. avg) * pa, or (league era - era) * ip for the rate stats. That's why I don't quite feel comfortable starting everyone at .000, 0 hr, 0 rbi, 0 sb, because the rate stats may deserve to be treated differently. Jim Cassandro's residual hits could work better here.
But think about it, the standard deviations for the top 300 players in a particular category (12 teams * 25 players) shouldn't be that much different than the standard deviations for imaginary/historical leagues. So why go to the extra work of adding to an imaginary team?