Decoding College Football: A Guide to Advanced Statistics
College football, with its unique blend of strategy, athleticism, and tradition, has increasingly embraced the world of analytics. While traditional stats offer a basic overview, advanced statistics provide deeper insights into team and player performance. However, navigating this landscape can be daunting. This article aims to demystify advanced college football stats, offering clear explanations and examples to enhance your understanding of the game.
Introduction to Advanced Stats in College Football
College football analytics aims to answer important questions using data. While other sports, like baseball, have been doing this for decades, college football is catching up. These advanced metrics move beyond simple counting stats to evaluate efficiency, explosiveness, and situational performance. By understanding these concepts, fans, analysts, and bettors can gain a more comprehensive perspective on the sport.
Key Statistical Concepts
Efficiency Metrics
Efficiency metrics evaluate how well a team performs relative to the opportunities it has.
Success Rate: This is a common tool used to measure efficiency by determining whether each play of a given game was successful or not. Specifically, it's the percentage of plays that are considered "successful," meaning plays with positive Expected Points Added (EPA). Think of it as consistency - are you regularly creating positive value on every play? In college football, success is often defined as gaining 50% of the necessary yards on 1st down and 70% on 2nd down. An opponent-adjusted version of success rate is called Success Rate+, which is built around a baseline of 100.0.
Marginal Efficiency: This adjusts basic efficiency (success rate) measures for down, distance, and field position. These factors create a baseline, and comparing a unit’s or player’s output to the baseline gives you a positive or negative number. For offensive players, the larger the positive value, the better.
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EPA (Expected Points Added): This measures how much a play (or series of plays) changes a team’s expected points. Positive EPA means the offense improved their scoring chances, while negative EPA means they hurt their scoring chances. EPA accounts for down, distance, and field position to measure the real value of each play. It forms the basis of ESPN’s Football Power Index (FPI) for college football.
PPA (Predicted Points Added): This is the same as EPA with one key difference: this stat uses predicted points instead of expected points, using projected points instead of the actual average points scored on a drive. A variety of stats are used to predict how many points the drive will end in.
FEI (Fremeau Efficiency Index): This considers each of the nearly 20,000 possessions every season in major college football. All drives are filtered to eliminate first-half clock-kills and end-of-game garbage drives and scores. A scoring rate analysis of the remaining possessions then determines the baseline possession efficiency expectations against which each team is measured. A team is rewarded for playing well against good teams, win or lose, and is punished more severely for playing poorly against bad teams than it is rewarded for playing well against bad teams.
Explosiveness Metrics
Explosiveness metrics measure a team's ability to generate big plays.
IsoPPP (Isolated Points Per Play): This is the Equivalent Points Per Play (PPP) average on only successful plays. This allows us to look at offense in two steps: How consistently successful were you, and when you were successful, how potent were you? It answers the question, "when you were successful, how successful were you?". Marginal Explosiveness adjusts IsoPPP for down, distance, and field position.
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Highlight Yards: A player’s per-carry highlight yardage is calculated as Highlight Yards divided by Opportunities, where opportunities mean only the carries in which the offensive line “did its job,” i.e., carries that went at least five yards.
Field Position
FP+ (Adjusted Field Position): Field position is presented through average starting field position (unadjusted) and FP+ (adjusted). You should remember to measure an offense by its defense’s starting field position, and vice versa. Special teams obviously play a large role in field position, but so do the effectiveness of your offense and defense.
Finishing Drives
Red Zone S&P+ (Adjusted Red Zone Efficiency): Finishing Drives are presented through points per trip inside the opponent’s 40 (unadjusted) and Red Zone S&P+ (adjusted). These measures look not at how frequently you create scoring opportunities, but how you finish the ones you create.
Turnover Metrics
Turnover Margin and Adjusted Turnover Margin: Using both Turnover Margin and Adjusted Turnover Margin, we can take a look at both how many turnovers you should have committed (on offense) or forced (on defense) and how many you actually did.
Win Probability
Win Probability Added (WPA): The change in win probability before the play versus after the play. WPA is a great tool for determining the value of any given play in terms of helping or hurting a team’s chances of winning the game.
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Post-Game Win Expectancy: Using Postgame Win Expectancy numbers for the entire season, you get a pretty good idea of how many games a team could have expected to win. Second-order wins basically say that playing the way you did, against the opponents you played, you would usually end up with a record of X, and it compares it to your actual record, Y. In the short-term, you can use second-order wins to gauge who may have been on the fortunate or unfortunate side of randomness for a given month, season, etc.
Offensive Line Specific Stats
Line Yards: A calculation that gives you an idea of how much the Offensive Line had to do with the success/failure of a run. The theory is that the first few yards of a run are mostly thanks to the gap opened up by the offensive line, but then anything beyond 5-10 yards is mostly due to the skill and speed of the running back. An opponent-adjusted version of the line measure is called Adj. Line Yards.
Stuffed Rate: This is the percentage of runs where the runner is tackled at or behind the line of scrimmage.
Opportunity Rate: This is the percentage of carries in which the offensive line “does its job” and produces at least five yards of rushing for the runner.
Defensive Stats
Havoc Rate: The percentage of plays in which a defense either recorded a tackle for loss, forced a fumble, or defensed a pass (intercepted or broken up).
Stop Rate: A defensive effectiveness metric that measures the proportion of defensive drives that ended in punts, turnovers, or turnovers-on-downs.
Pass Breakup Rate: A defensive personality stat that looks at the percentage of an opponent’s incomplete passes that you either intercepted or broke up.
Special Teams Stats
Field Goal Efficiency: Based on the length of a given field goal, an expected success rate is created, then multiplied by three points to create an expected point value. Field Goal efficiency compares your team’s output from field goals to the expected value, then divides it by the number of kicks.
Kickoff Efficiency: On average, kickoffs net around 40-41 yards.
Punt Efficiency: Based on where the punt takes place, an expected net punting value is created.
Other Advanced Stats
CPOE (Completion Percentage Over Expected): This measures the difference between a passer's actual completion percentage and their expected completion percentage, based on factors like pass location, quarterback pressure, and more.
Adjusted Stats (Adj.): Many stats are adjusted for the quality of the opponent. For example, Adj. Tempo accounts for both a team’s tempo (in terms of seconds per play) and the type of plays it runs.
Garbage Time Filter: To get a better picture of true team performance, many advanced stats filter out garbage time, which includes first-half clock-kills and end-of-game garbage drives and scores.
Five Factors: The sport comes down to five basic things, four of which you can mostly control. You make more big plays than your opponent, you stay on schedule, you tilt the field, you finish drives, and you fall on the ball.
Power Five/Group of Five: Used to differentiate between major conferences and other conferences.
Third-Down Conversions: Converting on third down is crucial to maintaining possession and scoring.
Fourth-Down Conversions: Going for it on fourth down can be a game-changing decision.
Understanding Ranking Systems
Several ranking systems utilize advanced stats to evaluate team performance. Here are a few prominent examples:
S&P+: A college football ratings system derived from the play-by-play and drive data. SP+ values are presented as a projected scoring margin over an average team in the current season.
FEI (Fremeau Efficiency Index): An advanced stat expressed as expected points per possession over an average opponent during the current season.
F+: An aggregation of the S&P+ and FEI systems into a single ranking.
FPI (Football Power Index): Utilizes EPA and SRS (Simple Rating System) to adjust for strength of schedule.
The Importance of Context
While advanced stats provide valuable insights, it's crucial to consider the context surrounding the numbers. Factors such as opponent strength, injuries, and weather conditions can all influence a team's performance.
Strength of Schedule
Facing tougher opponents can depress a team's stats, while playing weaker teams can inflate them. Adjusted stats help to account for these differences.
Margin of Victory
The data shows that teams with a larger average margin of victory tend to win more.
Randomness of Turnovers
Turnovers can have a large impact on margin of victory. A tipped pass near the goal line that the defense returns for a touchdown could be a 14 point swing. Since turnovers introduce randomness into the margin of victory that most computer rankings use, we need other metrics to evaluate a team.
Tempo
Looking at these numbers alone fails to account for both tempo and quality of competition.
Practical Applications
Understanding advanced stats can be applied in various ways:
Evaluating Team Performance: Identify strengths and weaknesses, assess efficiency and explosiveness, and compare teams on a more level playing field.
Predicting Game Outcomes: Use win probability metrics and ranking systems to make informed predictions.
Informed Betting Decisions: Identify value bets by comparing the probabilities suggested by the odds with your own calculated probabilities.
A Word of Caution
It is important to avoid common misconceptions and clichés. For example, fumbles rates strongly regress to the mean from early to late season. Also, it's important to remember that data analysis is not always about finding the most effective way to win; at its core, it’s about learning more about how the sport works.
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