As a football bettor, you want to have your best season ever. You'll consider any tool that can help you achieve this goal.
You've heard about football analytics, or Moneyball applied to America's favorite sport. It seems arcane though, and you'd rather cut off a finger with rusty garden shears than go back to math class.
However, analytics doesn't have to be about equations or messing with data in a spreadsheet. Done right, analytics can provide simple insights:
- How to spend your time handicapping NFL games
- Which college football rankings to consider, and which to avoid like the Zika virus
- How to make better preseason college football predictions
At SBR's first International Football Betting Conference, I gave a talk about 3 simple insights from football analytics. It got a lot of compliments, and I didn't notice anyone on their smart phone during the talk.
This article distills the essence of that talk. Let's get started.
1. The surprising truth about passing and rushing in the NFL
Before the 1991 Super Bowl, Bill Belichick told his Giants defense to let Buffalo running back Thurman Thomas rush for 100 yards.
As David Halberstam writes in Education of a Coach, it was a tough sell. The New York Giants played a physical defense that prided itself on not allowing 100-yard rushers.
No matter, the short, stout coach looked straight into the eyes of Lawrence Taylor and Pepper Johnson and said, "You guys have to believe me. If Thomas runs for a hundred yards, we win this game."
Did Bill Belichick go insane? I certainly thought so when I first read this story, but Belichick has analytics on his side. Let me explain.
We'll use numbers to evaluate the relative importance of passing versus rushing in the NFL. To make this comparison, we must move past misleading statistics like rush yards per game. Teams with the lead run the ball to take time off the clock. Any team can rush for 100 yards if they run it 50 times.
To measure true skill, let's consider yards per attempt, a powerful efficiency metric. A team can't fake their way to 5 yards-per-carry by running the ball more.
In the NFL, I'll define team efficiency for passing and rushing yards gained per attempt on offense minus yards allowed per attempt on defense. Better offenses gain more yards per attempt. However, better defenses allow fewer yards per attempt, so subtracting these smaller values leads to higher team efficiency.
The visual shows how NFL playoff teams excel in passing, either by throwing the ball on offense, preventing the pass on defense or both. Over a twenty year time period, 84.6% of playoff teams had a positive team pass efficiency. The majority of Super Bowl champions had elite values.
The importance of the passing game in the NFL should not be a surprise. Quarterbacks dominate the headlines while cornerbacks and pass rushers earn staggering salaries. However, the insignificance of rushing might surprise you.
From 1997 through 2016, only 57% of playoff teams had a positive team rush efficiency. The visual of rush efficiency for playoff teams shows a random scatter of points with both positive and negative values. A strong run game or stout rush defense has little effect in helping an NFL team win enough games to make the playoffs.
When you handicap an NFL game, put your time and energy into evaluating the passing game. For example, consider injuries. The quarterback is an obvious adjustment, and an injury to a top receiver matters too. But cornerbacks do not get the same attention even though they might be the NFL's most difficult players to replace.
2. How to choose computer rankings wisely
Back in the Dark Ages, college football had this system called the Bowl Championship Series to decide the two teams to play for a national championship. This BCS used a combination of human and computer polls to determine the top two teams.
The BCS had core beliefs. In no way would the system endorse running up the score by one team to impress humans or computers. In the name of sportsmanship, the BCS forbid computers from using margin of victory in their calculations. The computers could only take wins and losses as input.
This seemed like a lousy idea to me. Computer polls should rank teams so that a higher ranked is more likely to beat a lower ranked team on a neutral site. Throwing out the margin of victory seemed like judging a beauty contest based only on feet.
The BCS prompted me to do a study on banning margin of victory in computer polls. I calculated a series of computer polls, or rankings, based on different factors.
- Win percentage. Fraction of games won. Considers neither strength of schedule nor margin of victory.
- Colley Matrix A computer poll used by the BCS that takes wins and losses and adjusts for strength of schedule.
- Raw margin of victory. Points scored minus points allowed divided by number of games, a raw number that makes no adjustment for schedule.
- A least squares ranking system that takes margin of victory and adjusts for strength of schedule.
- The Power Rank An algorithm I developed that takes margin of victory and adjusts for strength of schedule.
Here's how often the higher ranked team won a bowl game from 2005 through 2014.
Consider two highlights from these results. First, the raw margin of victory outperforms the Colley Matrix. Despite all the beautiful mathematics involved with the latter, it can't outperform the simplest calculation based on margin of victory.
Second, the rankings that take margin of victory and adjust for strength of schedule perform the best. Look for these systems, like The Power Rank Sagarin and Dokter when doing your college football handicapping.
3. The reason accurate preseason college football predictions are possible
You want to predict the 2017 college football season with accuracy. Any analytics that can help pin down a win total for each team is helpful.
However, college football seems so random. The sport relies on 18-22-year-old boys raging with testosterone. At best, they throw an errant pass or miss a block. At worst, they punch a female. The inherent randomness of football, from tipped passes to bounces on fumbles, makes college football even more of a mess.
Despite these factors, a sneaky factor makes it possible to predict college football in the preseason with surprising accuracy.
This visual shows the rating, or expected margin of victory against an average FBS team, for college football teams in 2015 compared with 2016. A point along the diagonal line implies that a team had the same rating in both years.
The visual shows how teams tend to have the same rating from year to year. Due to financial resources, recruiting and other factors, college football teams persist from year to year. Blue blood programs Alabama do not trade places with bottom feeding programs like Rice.
This persistence of teams from year to year allows for accurate preseason predictions for college football. My preseason rankings come from a regression model that considers four years of team performance based on The Power Rank algorithm, turnovers and returning starters.
Over the past 3 seasons, my preseason rankings predicted the winner in 70.8% of games (1452-598 with no prediction in 235 games). This percentage includes only games with two FBS teams and excludes the more predictable FCS cupcake games.
Get the college football win totals report
In The 2017 College Football Win Totals Report, I use this preseason model and a team's schedule to calculate a win total for all 130 FBS teams. It's a critical resource for finding value in the markets.
In addition, I identify the sneaky resource you want to use in addition to these numbers to help your handicapping.
To get your free copy of The 2017 College Football Win Totals Report, click here.