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Georgia went in , a record acceptable only for new coaches in Athens. To start , they landed at 15th in the preseason AP poll. So what does regression say about these two teams? Each year, I put together a preseason college football model that uses regression on many variables.
In college football, team performance tends to persist from year to year. Programs like Alabama have financial resources and traditions that Rice will never have. These teams will not swap places in the college football hierarchy. Hence, my preseason model uses the past 4 years of team performance to predict the next season. This part of the model says that team is most likely to perform as some combination of their last 4 years, with recent years weighted more.
This makes the model cautious about an outlier season or 9 games. The preseason model also considers turnover margin. Turnovers can impact the scoreboard, as a key fumble halts a game winning drive, or an interception returned for a touchdown turns a tight game into a laugher. However, turnover margin regresses to the mean of zero from year to year in college football. Hence, the model uses turnover margin in each of the past 4 seasons.
Last, the model considers returning starters. More experience implies better performance for college football teams. Over the past 3 season , this regression model for college football has predicted The model only makes predictions for games with two FBS teams.
Heading into , the preseason college football model had USC 16th. In the previous 4 seasons, USC had never finished the season higher than 14th in my college football rankings. Despite their impressive finish in , they only rose to 14th because of a poor start. However, 4th in the AP poll seemed too optimistic. In contrast, the regression model agreed with the AP poll on Georgia.
The model had the Bulldogs at 18th while the polls had them at 15th. Analytics and polls agreed on Georgia as a solid top 25 team but not a playoff contender. During the actual season, USC did not live up to their top 4 ranking.
They dropped an early road game at Washington State. Then their playoff hopes ended when Notre Dame stomped their defense for 8. However, this turned into a blessing, as true freshman Jake Fromm took over and had a brilliant season. The defense also improved, as they jumped from a solid 28th in my adjusted yards per play in to an elite 3rd in This unit had only one bad game when they allowed 40 points at Auburn.
However, they atoned for this blip by holding Auburn to 7 points in the SEC title game win a few weeks later. Georgia made the College Football Playoff, and they took Alabama to overtime in the championship game before losing. This analysis combines my regression model with knowledge of programs. The two teams, Penn State and Oklahoma State, finished lower in the final poll than the preseason poll in A regression model is unlikely to identify breakout teams like Georgia in Their true freshman quarterback worked out, and their defense made a leap.
There is a random element to teams that make a sudden rise. These predictions are more difficult than finding an overrated team by a regression model. First, quantities in football like turnover margin show very little persistence from an earlier period to a later period. These quantities regress to the mean, as your best guess for the later period is the average. To find football teams that might not be as strong as their record suggests, look for teams with large, positive turnover margin.
In contrast, teams with large, negative turnover margin might be better than their record. Use these links for data on college football and the NFL. Based on data from the through season, the visual shows that one year explains 1.
A team like Oakland, who went in one score games in , should not expect the same good fortune again. The Raiders went in one score games in Second, some quantities like team strength in college football tends to persist from year to year.
This allows for predictive models based on linear regression. Even with this persistence, the models still predict regression for outlier performances, both good and poor. The stretch for USC to end serves as an example. However, regression models can not predict teams that jump from ordinary to the outlier, like Georgia in Use these rankings as a guide to find overrated teams near the top of the polls. For school I am writing something like a thesis, about the predictability of sport.
I am trying to predict soccer, basketball, baseball, american football and ice hockey the 5 most popular team sports in the US. Afterwards i will compare the results and try to find out what causes the difference in predictability. What i struggled with most until now was choosing which statistics to use for my predictive models.
I still have to learn a lot about predictive modeling and this article and the other articles on this site have helped me enormously. The article is very well written and explains everything very clear. Just to be sure: if you want to find out which statistics will be useful for your model you have to make a regression between two different years?
Consequently if there is a strong correlation betweent the two it would be a good predictor? I am asking this question becuase I was under the impression that a correlation between the value you want to predict and an independed variable would imply that it would be useful for predicting. I recommend looking at variables such as margin of victory and turnovers and see if there is a correlation, both year to year and early to late season.
Then you get a baseline for whether to use the variable. You can also test you models with partial F-tests. Meaning you can run the full model and selectively remove variables. This is known as nested models. Nested models remove the variables you wish to test and the summed R values validate your thought process. It is a great way to quickly test variables, especially with R or some other software.
Example: You are testing a full model prediction on an NFL team. This is how I approach any new concept for model building and I further enhance them based on new information or new stats introduced by the geeks.
This makes it possible to use linear regression to predict the season. Ed Feng blog post on How to make accurate football predictions with linear regression. For a simple, visual explanation of the previous statement, click here. You might as well guess at […]. For a primer on regression analysis in sports, see this article. Your email address will not be published.
This site uses Akismet to reduce spam. Learn how your comment data is processed. While I usually save my best predictions for paying members of the site, I offer a sample in my weekly email newsletter. Comments Dear Ed Feng, For school I am writing something like a thesis, about the predictability of sport.
Great stuff, Ed. I really enjoyed the article and will spend more time with it. Trackbacks College football win totals for says:. August 17, at am. We are displaying the results of our research in the form of scatter charts. This makes it much easier for you to visualize the data. In terms of a strong correlation, you would want to see the data points clustered in a rising line.
The more spread out the data points, the weaker the correlation. There is only a slight positive correlation between bigger totals and variance in final total points. The variance in games with a total over is nearly a full point more than ones under There is a very significant difference between games with totals over and those under. We found the strongest relationship between bigger totals and more variance in final total points in college basketball. There are some lower numbers that look as if they have high variance.
This is sometimes due to a smaller sample size. By and large college hoops games follow exactly what we expected. The totals become less predictable as the total gets bigger. There is no significant correlation in the NFL. We find this happens in just about everything pertaining to the NFL in terms of variance.
The good new is that, because there is less variance in the NFL, it is a great league to play teasers on. There is also a pretty solid association between rising college football totals and the variance of final point totals. This is particularly evident in totals over 54 points. Here the variance is a point and a half more than in totals under 54 points. This follows the logic that when more points are expected to be scored in a game, it is harder to predict the final total number of points.
The biggest take away from this data is that the college game is much more unpredictable than pro sports. The NFL, as expected, is the most predictable of all of the leagues we examined. This just goes to show how good oddsmakers have gotten at setting totals in NFL games.
How can we use this information to our advantage? First and foremost, the bigger the standard deviation variance a number has, the less predictable it is.
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