Football Forecasting - Summary & Final Thoughts - EdsCave

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Football Forecasting - Summary & Final Thoughts

Football Forecasting

In the previous pages we have examined a couple of football game forecasting methods, ranging from utterly trivial to moderately sophisticated, with the underlying feature that the only input information is the final scores for each game, and which teams played.  The following table summarizes the accuracies of the different methods when applied to the 2012,2013 and 2014 NFL seasons.




Method

% Games Called Correctly (wks 5-20)

2012

2013

2014

2012-14

Flip a Coin (presumed accuracy)

~ 50 %

~ 50 %

~ 50 %

~ 50 %

Home Team wins

56 %

58 %

57 %

57 %

Previous Games won %

63 %

60 %

66 %

63 %

   "        " with home-team adjustment

64 %

61 %

68 %

64 %

Mean Points Per Game

66 %

54 %

63 %

61 %

"        " with home-team adjustment

68 %

60 %

66 %

64 %

Margin-of-Victory Model

66 %

63 %

68 %

66 %

Offense-Defense Model

66 %

65 %

64 %

65 %

One thing that jumps out is that even though a season has ~250 games to predict, random chance still plays a part in prediction accuracy. One can see a few percentage points variation in accuracy for each method from season to season. Evaluating a prediction algorithm's effectiveness over even shorter periods, such as a single game week, or even half a season will give even more sporadic results.  To get a reliable measure of predictive accuracy,  any algorithm being considered for serious use should be back-tested over at least several years of data.

Another question I am sure you are asking yourself, (and I have asked myself) is how good these algorithms really are? To this point I have just compared' algorithm vs. algorithm'. The real test is 'algorithm vs. football expert'.  Fortunately, ESPN provides a series of game predictions from a group of human experts and a crowdsourced expert (Pick'em). This data can serve as a meaningful benchmark.  Here is a summary of % correct calls for the 2015 season covering weeks 5-16.  Although ESPN's experts started at week 1, I adjusted the performance from week 5 to provide a direct comparison to the algorithms, which require a few weeks of startup data.

2015 Weeks 5-16 Forecast Ranking
Forecaster data from ESPN NFL Expert Picks
http://espn.go.com/nfl/picks

Forecaster

% Correct Calls

Mortenson

65.0 %

Hoge

65.0 %

MOV Model

63.8 %

Jaworski

63.3 %

Pick'em

62.7 %

OD Model

62.7 %

Schlereth

61.0 %

Caplan

61.0 %

Joyner

60.5 %

Ditka

60.5 %

Carter

60.5 %

Golic

59.9 %

Johnson

58.8 %

Jackson

58.2 %

Allen

57.1 %

Wickersham

55.9 %

As can be seen, both the Margin-of-Victory and Offense/Defense models provide comparable performance to human experts. The major downside of the algorihtmic models is that they can't provide the play-by-play analysis or game narrative that are such a big part of the sport.  Somehow, I have the feeling that 'robo-analyst' will not be part of the game for a long time.

And for the final time, the information provided on these pages is intended for entertainment purposes only, and not for purposes of gambling, as the accuracy of the models presented here is not adequate to 'win' (make money) on a consistent basis in Las Vegas style gambling. If any of these forecasting models were that good, what would I be posting them here for? I would be on the Boston-Las Vegas flight every Friday night during football season making some serious money!!!

2 JAN 2016

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