Motivation

I wanted a metric which answers the question: on average, how much does a team over/under-perform in games compared to their opponents typical result? For example, if Team A loses to Team B by 10 points, but Team B typically wins by 20 points, Team A over-performed by 10 (-10 + 20) points. So, for this game Team A would earn 10 points. Then across all games, their results would be average.

Additionally, I wanted a metric to acknowledge that winning (or losing) is also an important aspect (not just margin of victory). So, if Team A beats Team B, and Team B wins 90% of their games, Team A should be awared further. So, the game metric becomes: -10 + 20 + (N * .9), where N is left to be determined.

Methodology

Each team is ranked based on their average performance from each game (game metric). For each game, the game metric is calculated as:

• margin of victory (negative if a loss) +
• (w * opponent's average margin of victory (not including game(s) played against this team)) +
• (result factor = (x1 * opponent win percent (if win) or -x2 * opponent loss percent (if loss) (not including game(s) played against this team)))

Here, w=.5, x1=10, x2=10, but they are tweakable constants.

Further, the raw score of each game is adjusted by:

• The winning team gets +5 points
• The away team gets +2 points
• If an FBS team loses to an FCS team, the victory margin is multiplied by 3 (after the previous adjustments are made)
• FBS wins vs. FCS teams are completely ignored

Again, each of the mentioned constants are tweakable.

Note: calculating the "result factor" of the game metric is used by counting real-life wins/losses; the point adjustments listed above only affect the first two factors of the game metric.

Usage

There are two main programs:

• rank.py: Used to compute a ranking, or compare rankings.

  python3 rank.py <team_results_directory>

  python3 rank.py team_pages/2022-week6-results/

  This has a function to compare a ranking to another ranking (change in Top 25, biggest movers) and another function to filter a ranking based on conference.

  Note: before running for a new week of results, first craate the results directory under team_pages, then it will populate the directory.

• predict_analyze.py: Used to predict a schedule of games based on a previously generated ranking, or analyze a schedule of results and sportsbook predictions in relation to a ranking.

  python3 predict_analyze.py <ranking_filename> <predict_analyze> <optional: espn_schedule_url>

  python3 predict_analyze.py rankings/2022-week6.txt predict

  python3 predict_analyze.py rankings/2022-week6.txt analyze https://www.espn.com/college-football/schedule/_/week/9/year/2022/seasontype/2

  Note: To capture the pre-game view of game pages, just run with the analyze option on the appropriate schedule before the games have started. These will be saved under ./preview_game_pages

Current Top 25

1. Georgia (+0)

2. Michigan (+0)

3. Ohio State (+0)

4. Alabama (+1)

5. Utah (+4)

6. Tennessee (+2)

7. Penn State (+0)

8. TCU (-4)

9. Clemson (+1)

10. Kansas State (+1)

11. USC (-5)

12. Florida State (+0)

13. Oregon (+0)

14. Texas (+0)

15. Washington (+1)

16. Oregon State (-1)

17. Tulane (+1)

18. UTSA (+3)

19. UCLA (+0)

20. Illinois (+0)

21. Troy (+1)

22. LSU (-5)

23. South Alabama (+1)

24. Mississippi State (+1)

25. Notre Dame (-2)

    Dropped from last Top 25: None

Track Record

FBS Games

Season, Week	Games Registered	Games Correct	Percent Correct	Games Differ Sportsbook	Games Differ Sportsbook Correct	Percent Differ Sportsbook Correct	Amount on ML	Profit From ML	Percent Profit ML
2022, Week 14	12	8	66.67%	2	0	0.00%	$1,100	-$206.38	-18.76%
2022, Week 13	63	43	68.25%	7	2	28.57%	$5,900	-$458.01	-7.76%
2022, Week 12	65	43	66.15%	7	1	14.29%	$5,800	-1,045.07	-18.02%
2022, Week 11	64	43	67.19%	11	4	36.36%	$6,100	-$641.04	-10.50%
2022, Week 10	60	39	65.00%	11	4	36.36%	$5,800	-$350.67	-6.04%
2022, Week 9	47	36	76.60%	9	5	55.56%	$4,600	$513.16	11.16%
2022, Week 8	52	32	61.54%	14	6	42.86%	$5,200	$139.75	2.69%
2022, Week 7	51	30	58.82%	7	2	28.57%	$4,400	-$925.57	-21.04%
2022, Week 6	57	39	68.42%	11	4	36.36%	N/A	N/A	N/A
Totals	471	313	66.45%	63	25	39.68%	$38,900	-$2,973.83	-7.64%

Notes:

• Games Registered includes only FBS vs. FBS matchups for the week for 2022 Week 6 through 2022 Week 12.
• Sometimes ESPN doesn't include sportsbook data (example) or sportsbook doesn't have a favorite (example).
• 2022, Week 7 was the first week collecting moneyline data and is incomplete due to data collection after some games had been played.

FCS Games

Season, Week	Games Registered	Games Correct	Percent Correct	Games Differ Sportsbook	Games Differ Sportsbook Correct	Percent Differ Sportsbook Correct	Amount on ML	Profit From ML	Percent Profit ML
2022, Week 14	10	10	100.00%	0	0	100.00%
2022, Week 13	10	5	50.00%	2	1	50.00%
2022, Week 12	63	47	74.60%	7	4	57.14%
Totals	83	62	74.70%	9	5	55.56%

Ranking Methodology Change Log

2022, Week 12 Rankings:

• Began ranking FCS teams along with FBS teams
  • Removed 21 point adjusted scoring penalty in FBS vs. FCS games
  • Apply a 0.25 weight to adjusted scoring margins for FCS vs. FCS wins (and 1/0.25 weight for FCS vs. FCS losses)
• Lowered adjusted scoring margin cap from 38 to 28
• Lowered recency bias from 0.05 to 0.03

2022, Week 9 Rankings:

• Major bug fix: neutral site games no longer double counted.

2022, Week 8 Rankings:

• Remove 3x multiplier for FCS losses, replace with 21 point adjusted scoring margin penalty vs. FCS teams
• Consider all FCS games, not just losses
• Apply recency bias 0.05
• Apply adjusted scoring margin cap at 38

Base:

• Ignore FCS wins
• Apply a 3x multiplier to adjusted score margin in losses vs. FCS teams
• Away team receives 2 points in adjusted score
• Winning team receives 5 points in adjusted score
• Win factor: 10, loss factor: 10 in calculating game metric
• Opponent strength weight in calculating game metric: 0.5