Date: Fri, 4 Oct 1996 10:03:41 -0600 (CDT) > Interesting how you rank the teams. You don't take quality of opponent > into consideration? Just offensive and defensive prowess? So you can > have Nebraska #3 and ASU, who totally dominated the huskers, #11? ASU > has beaten 3 ranked teams this year (#1 Nebraska, #18 Washington, and #25 > Oregon). The model does take the quality of the opponent into account THROUGH its offensive and defensive model fits. Lets look at Arizona State for example. Here are the model vs actual scores for ASU using the model's latest fit: prob rnk winning_team actual pred rnk losing_team actual pred 71% 16 Arizona State 45 32.7, 19 Washington 42 26.5 99% 16 Arizona State 52 52.3, 109 North Texas 7 6.0 12% 16 Arizona State 19 27.7, 3 Nebraska 0 41.3 89% 16 Arizona State 48 38.9, 45 Oregon 27 23.7 (where "pred" is the AVERAGE score the model predicts for that matchup). The model finds offensive and defensive fits for each team which on the average produce the best fit to the actual scores. With the exception of the number of points Nebraska scored, the actual vs model scores are pretty close. If we look at the average performance: Average points ASU was expected to score: 38 actual average points: 41 Average expected defensive points: 24 actual defensive points: 19 The fact that the model did not reproduce a low score for Nebraska suggests that this was an unusual event (i.e. far from the average behavior of the two teams). When finding the offensive and defensive fits, the model goes through several iterations of adjusting the off/def for each game to match the score and then averaging the off/def of each team. Getting the model's Nebraska points against ASU closer to zero would require raising ASU defense and/or lowering Nebraska's offense. The fitting does this to the extent that this is ALSO consistent with their other games. > I can't see rewarding schools for scheduling patsies and then > crushing the living daylights out of them. The model doesn't reward playing against patsies. If a team plays a patsy it must score a lot of points or drop. It is how many points that are scored that counts, not who wins. For example: prob rnk winning_team actual pred rnk losing_team actual pred 99% 3 Nebraska 55 47.2, 27 Michigan State 14 21.6 78% 3 Nebraska 0 41.3, 16 Arizona State 19 27.7 99% 3 Nebraska 65 50.5, 59 Colorado State 9 14.7 When playing Michigan State and Colorado State, if Nebraska had not done as well as they did the model would have lowered its off/def. Average points Nebraska was expected to score: 46 actual average points: 40 Average expected defensive points: 21 actual defensive points: 14

Date: Wed, 2 Sep 1998 14:29:45 > ... How in the world can you justify a prediction based on LAST YEAR'S TEAMS! > This is college football. Seniors graduate ... Actually, making this assumption at the beginning of the year is not such an awful thing to do. In 1996 my model picked 75% of the in the first five weeks of the season (when the assumption of last year's performance has the largest effect). This is the same percentage as for the entire 1996 season. Last year, when I made an attempt at adjusting the initial values to reflect changes from the previous season, the model picked 77% in the first 5 weeks and 78% for then entire year. Basically, it is not that bad of an assumption. > ... Unless a computer > ranking system can be devised that evaluates the teams as they are now ... > [it] really means nothing when it comes to the relative strengths of > the teams. This is precisely what my model is designed to do, once enough games have been played in the season (say about 5 weeks). In the mean time I recommend that you use the model prediction as a baseline to adjust according to how you feel the teams have changed.

Date: Tue, 29 Sep 1998 12:48:14 > Now that you've added this Uncertainty value...how are you coming up with > this number? There is no completely unique way fit the model to the actual scores. This season I am now doing the fit three different ways. The variation in rank between these methods is what I list as the "uncertainty". At the beginning of the season the uncertainty is high because the solution depends strongly on the initial guess at the teams strengths. As more games are played the different methods tend to converge toward a common solution, reducing the uncertainty.

Date: Thu, 23 Sep 1999 14:27:50 > ... Your predictions include a random component (0 to 1). 10,000 games > are played and the scores averaged. ... Do you actually simulate > 10,000 games? I ran 10,000 games each for a range of offensive and defensive values and constructed a table. For any given game I look up in the table under the offensive and defensive settings for the two teams and get the average scores and probability of a win. So it has the same result as if I did run 10,000 games but all I have to do is retrieve from a table.

Date: Mon, 21 Aug 2000 14:12:32 > Thanks for the info. 76%, roughly 3 out of 4, is a pretty good track > record. But it seems to me that a lot of games are in the 80-20 range, and > you'll get a very high percentage of them right. So, my question is: Of > all the games that your model predicts higher than 40% for the loser and > less than 60% for the winner, how many does your model get right? And how > about the 70-30, 80-20, 90-10? I was just curious to see how the winning > percentage is reflected. Good question. Here is how my model predictions break down according to model forecasted percentage brackets: 50-59% 60-69% 70-79% 80-89% 90-99% all total games: 449 547 747 685 703 3139 number right: 268 355 541 563 639 2374 number expected: 248.3 355.1 563.8 584.2 657.3 2416.4 expected %: 55.3 64.9 75.5 85.3 93.5 77.0 actual %: 59.7 64.9 72.4 82.2 90.9 75.6 For example, for games where my model forecast a 60-69% change of a victory (547) it got the winner right 355 times. The average expected percentage was 64.9% (355.1 of 547) and the actual correct also was 64.9%. Actually it is not a coincidence that the actual and expected percentages are so close. I have adjusted my forecasted percentages (reducing the larger ones by about 5-6% and increasing the lower ones by 4%) so that the forecasted and actual percentages would be the same. I thought that this was pretty impressive until I realized that I could come up with another model that was 100% accurate: flip a coin. Expected correct is 50% and actual correct will also be 50% on average. The difference is that the coin-flip model is totally useless. That is why I gave my overall accuracy. The better the model, the higher that will be. I would love to know what the absolute best percentage is. If one knew that, then one would have the ultimate model. Since I lower my larger percentage forecasts by about 5%, perhaps the actual average percentage of favored team over underdog is around 80%.

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