Using Gambling to Improve Fantasy Football Predictions
Updated: Jan 22
This post was inspired by a Reddit post a few weeks ago. u/showmeurknuckleball asked if targeting kickers in games with the highest over/under total was a viable strategy. The comments mostly agreed (though with some debate about whether lower scores indicated more field goals), but no one seemed to have the stats to back it up. Today's post will aim to answer that question using data from 2019.
First, we need to get the data. For fantasy data, we will use the espn-api maintained by Christian Wendt to pull the fantasy projections and results of all players (we'll explore beyond kickers) in my standard scoring ESPN league. For the gambling data, we can use this dataset from Kaggle. The question will be whether using over/under totals (O/Us) is a better predictor of player performance than ESPN's projections. (Note to create a more reasonable assessment, I have limited which kickers are eligible to the top 20 projected kickers. Considering most leagues are only 10 teams and only start/roster 1 kicker, this sample should include all kickers being considered. I have repeated the analysis to include all kickers and the results are a little noisier but tell the same story.
Scatter plots with regressions of kicker point totals vs game over-under (left) and projected points (right). The shaded area shows a 95% confidence interval of the regression
Interestingly, graphically there seems to be a better (tighter) relationship between game over under and kicker points than there is between the projected points and actual points. We can test this statistically, and the stats match with the eye test. ESPN's player projections explain about 1% of the variation in actual points scored. In comparison, Game O/Us are significantly correlated (p = 0.001) and explain about 7% of the variation in actual points scored. We can also consider both game O/Us and player projections with a multivariate linear regression, which does improve our regression, but barely (explaining 7.1% of the variation). Interestingly, the game O/Us account for 92% of the combined prediction power.
But here is the thing. We don't care about how well game totals predict fantasy points, we care about whether it predicts the top players. To compare this, we can compare the total points scored by the top 10 projected kickers to the total points scored by the kickers in the top 5 over under games (2 kickers per game so 10 kickers total).
Violin plots of the top 10 kicker performance when chosen based on ESPN projections, Game Over/Under, or a combination of the two. Box plot in the center with the white dot at the median of the sample. The vertical redline marks median performance based on ESPN's projections.
As we can see, choosing just based on game O/Us does outperform ESPN's projections. Specifically, a top 10 projected kicker by ESPN averages 7.4 fantasy points. The kickers playing in the top 5 Over/under Games average 8.2 fantasy points. However, both methods have a lot of variance (both have a standard deviation around 4) and as such the two samples are not statistically significantly different (p = 0.07). Now, if we use a combination of the two as discussed above, it does perform a little better, averaging 8.4 points and the combo is statistically significantly better than ESPN's predictions alone (p = 0.02).
All of that is to say that I think choosing the kickers in the higher over under game is a completely viable strategy and one that on average did outperform ESPN's projections in 2019. Even more convincing is if you are debating between 2 kickers, then considering the game over under will give you a statistically significant advantage.
But what about other positions?
Violin plots for QB, RB, WR, TE, and DST. QB, TE, DST are the top 10 predicted scorers based on ESPN's projections, Game O/Us, or the combination of the two. RB and WR are the top 20 predicted scorers. The vertical redline represents the median points cored by ESPN's projections.
Starting with the other single-starter positions (QB, TE, and DST), in all three both ESPN projections and the game O/Us are significantly correlated with point production. For QB's and TE's, ESPN's projections are a better predictor and account for about 90% of the predictive power when we combine the two (as we did with kickers). Interestingly, for defense, the two are about even. Game O/Us explain about 4% of variation while ESPN's predictions explain about 5% of the variation. Together, they predict about 8% of the variation, and the top 10 defenses based on the combination produce about 9.5 points a week (compared to 8.3 points for ESPN and 8.2 points for the 10 lowest Game Over Unders). This result suggests that you should consider game over under when deciding between defenses, but solely relying on the game over/under won't produce better results than ESPN's projections. One reason for this may be lopsided games, but I have not explored that data.
For the RB and WR positions, I think unsurprisingly, we see less usefulness from the game O/U. I think this is driven by the increased options in the real world such that any effect of the game O/U is distributed between a few players and therefore does not significantly affect individual player performance. Even in combination, ESPN projections account for about 95% of the predictive power.
Limitations of this analysis
The biggest limitation is that I haven't explore implied point totals for a team. Some of the higher over under totals may be driven by one team, in which case only one side of the game is worth playing. I did not look into it, but it is certainly worth future analysis.
Putting it all together
In conclusion, Using the Game Over Unders is not only a viable strategy for picking kickers, but is actually a better strategy than just using ESPN's projections. Additionally, Game Over Unders provide valuable information for choosing defenses, though that should be done in combination with ESPN's projections, rather than instead of.
Finally, as immediate application, 5 of the kickers in the top 5 over under games this week are owned in less than 50% of ESPN leagues. Specifically, Joey Slye (Car), Daniel Carlson (LV), Jason Myers (SEA), Cody Parkey (CLE), and Dan Bailey (MIN). I've listed them in order based on ESPN's projection as well so if you need a kicker this week, I'd look at some of these guys. Of course, this method is not perfect (as Joey Slye already played this week and underperformed after I played him in a few leagues).