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.
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.
There are 3 main programs:
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espn_team_pages.py: Used to retreive each team's ESPN page and store them in the specified directory.
python3 espn_team_pages.py team_pages/<sub_dir> -
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.
NOTE: This probably doesn't work now, as ESPN has in-housed their betting info. Probably in a different format.
python3 predict_analyze.py <ranking_filename> <predict_analyze> <optional: espn_schedule_url>python3 predict_analyze.py rankings/2022-week6.txt predictpython3 predict_analyze.py rankings/2022-week6.txt analyze https://www.espn.com/college-football/schedule/_/week/9/year/2022/seasontype/2Note: 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
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Georgia (+0)
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Michigan (+0)
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Ohio State (+0)
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Alabama (+1)
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Utah (+4)
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Tennessee (+2)
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Penn State (+0)
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TCU (-4)
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Clemson (+1)
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Kansas State (+1)
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USC (-5)
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Florida State (+0)
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Oregon (+0)
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Texas (+0)
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Washington (+1)
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Oregon State (-1)
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Tulane (+1)
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UTSA (+3)
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UCLA (+0)
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Illinois (+0)
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Troy (+1)
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LSU (-5)
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South Alabama (+1)
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Mississippi State (+1)
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Notre Dame (-2)
Dropped from last Top 25: None
| 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.
| 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% |
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