Death star versus young Jedi, an Alabama and Clemson preview

deathstarCan Clemson upset Alabama in the championship game of the College Football Playoff?

It seems unlikely. Alabama is the most consistent, elite team in college football. Through both recruiting and weekly preparation, Nick Saban puts his team in national title contention year after year.

Some years, the Crimson Tide get derailed. It takes a miracle play like the Kick Six against Auburn, or an elite performance from Ohio State’s offense in last year’s playoff semi-final.

But Alabama is college football’s empire, a finely oiled machine with infinite resources to destroy the opponent. Their defense is a Death Star aimed at Clemson and another national title.

Not many expected a national title for Clemson this season. They were ranked 12th in the preseason AP poll, as they lost most of their starters on an elite defense and had worries about the health of Watson.

However, Clemson has played exceptional this season, and QB Deshaun Watson has taken a starring role. He’s a young Jedi beginning to use his full powers, just like Luke Skywalker in the New Hope.

Can he blow up the Death Star and win the national title? Let’s look at the match ups and possible value in markets.

Alabama’s offense against Clemson’s defense

Clemson’s run defense had an outstanding game against Oklahoma by allowing 3.9 yards per carry. However, don’t expect the same against Alabama.

For the season, Clemson has allowed 4.6 yards per carry (numbers do not include sacks like usual college football statistics), 42nd in the nation. The rush defense is good but not elite, and they played one of their better games against Oklahoma.

Don’t expect the same type of performance from Clemson in the title game against Alabama. They face Heisman trophy winner Derrick Henry in an offense that mostly runs the ball.

Overall, the numbers see a very even contest between these units. This usually implies Alabama will score about 28 points, the college football average. The Power Rank’s member numbers predict 26.6 points, a slight adjustment for the slow pace at which Alabama’s offense plays.

This number most likely requires a subjective adjustment for Clemson defensive end Shaq Lawson. The pass rush beast racked up 10.5 sacks and 23.5 tackles for loss this season but hurt his knee against Oklahoma. Lawson most likely plays on Monday night but probably not at 100%.

Alabama’s defense and possible value in the under

Clemson ran for 320 yards on 5.7 yards per carry against Oklahoma in their semi-final win. QB Deshaun Watson contributed to this efficient total, as he broke off a 46 yard run in the second quarter.

There are two reasons Clemson won’t get anywhere near these numbers against Alabama.

First, Clemson hasn’t been efficient with their ground game. For the season, they have rushed for 5.2 yards per carry, 51st best in the nation.

Second, Alabama’s front seven is dominant. They have allowed 3.5 yards per carry (numbers do not include sacks like usual college football statistics), second best in the nation. LSU’s Leonard Fournette had 31 yards on 19 carries against this unit.

The numbers like Alabama’s Death Star defense to contain Clemson’s offense, as my member model predicts 20.2 points for Clemson. A predicted total of 46.8 suggests that under 50.5 (as of Thursday morning) has value.

However, the under doesn’t feel right in this game. Deshaun Waton is the best college football player in the nation not named Christian McCaffrey (no Stanford bias whatsoever). His blossoming talent gives Clemson a chance of blowing up the Death Star.

A few other factors go against the under. I mentioned the injury to Clemson defensive end Shaq Lawson in the previous section. Also, Alabama’s Tony Brown is suspended for this game. The cornerback has a limited role in the secondary but has been making special teams plays all season.

Can Clemson pull off the upset?

It’s unlikely. The Death Star usually wins; they only make movies about the statistically improbable. (Cue Han Solo voice about never telling him the odds.)

A Clemson win wouldn’t be a fluke. My numbers make them a 6.4 point dog, which corresponds to a 32% win probability. However, Alabama will most likely wins their 4th national title in 7 years.

Computers vs human judgment in picking college football games

Since the beginning of the 2014 season, The Power Rank’s college football prediction service is 56.4% in picking spreads and totals (137-106-6). Mike Craig and I use a computer model based on my algorithm that makes accurate adjustments for strength of schedule.

However, we never only rely on computers and numbers to make predictions. We always consider subjective factors like injuries and situations.

This reliance on both computers and human judgment is reminiscent of the world of chess and a New Yorker article on Magnus Carlsen, the 23 year old World Champion from Norway.

Computers and Chess

For most of the 20th century, the Soviets dominated chess. They relied on “focus, logic, and, above all, preparation” to produce world champion after world champion.

However, they met a new foe in the late 1990’s: the computer. These inanimate objects are all about focus and logic. When computing power hit a certain level, computer programs could use brute force search to pick the best move. This led to the Deep Blue computer’s famous win over World Champion Gary Kasparov in 1997.

Nowadays, computers always beat the top human players. When asked what strategy he would bring against a computer, a grandmaster said “I would bring a hammer.” Computers are also a training tool for chess players.

But Magnus Carlsen is different. As he told The New Yorker, the Norwegian champion never trains with a computer. He admits to preparing less for tournaments and relying more on his judgment.


Predicting football games

Predicting football games is more complicated than winning a chess match. Players don’t occupy a square on a board. Football also adds the element of randomness, as events like fumbles defy any quantitative attempts to predict them.

This makes human judgment even more important in predicting college football games than chess. For the college football prediction service, we always consider subjective factors like injuries and situations. For instance, we needed to make an adjustment for Baylor as they will start third string QB Chris Johnson against North Carolina.

Both computers and human judgment play a role in giving you the best possible predictions.

Last night to take advantage of bowl season discount

The prediction service, which has gone 55.9% (66-52-2) in 2015, is available for bowl season. The price goes up at midnight, Eastern time on Tuesday, December 22nd, 2015.

To take advantage of this early bird price, click here.

Mailbag: Do bookmakers shade the under in MLB totals?

Thank you to everyone who submitted questions. You can read the first part of this mailbag here.

MLB totals for 2015

Why do you suppose the bookmakers shaded the unders in MLB for April 2015? I don’t follow baseball that closely, but there seems to be a lot of press about scoring being down and the games being too long. Does speeding up the game increase scoring?

Betting over on every game in April would have yielded +49 units in 2015.

Average team scoring is up (4.27 runs vs 4.21 runs) from April 2014, but the avg total line is down (7.63 vs 7.84).

— David Sone

Thanks for the analysis. I bet the bookmakers are a bit cautious about high numbers in April due to uncertainty in pitchers and the opposing offense.

I ran some numbers for May 1st through June 11th. This analysis considers the median closing total for each game.

The edge in taking the over is gone, as more games went under (285) than over (261). The market total nailed the total 26 times in 572 games.

The average market total is back to 7.86 during this period, while there have been 8.09 runs scored per game.

The best efficiency metric for college football

If you had to single out one certain variable that is most important for college football betting/predictions, what would you say?

— Lance Stone

There are a lot of choices for college football statistics, but I personally like yards per play for predicting college football games. This stat is incredibly easy to calculate and is mostly immune from the randomness of turnovers.

In college football, you need to be careful in breaking down this statistic into rushing and passing. On all major (and minor) media sites, sacks count as rushes even though the offense intended to pass. At The Power Rank, I count sacks as pass attempts in my yards per play rankings.

To make game predictions, I take yards per play and adjust for strength of schedule with my ranking algorithm. These rankings give one of the many predictions I use in the ensemble predictions available to my members.

There are other efficiency metrics such as expected points added and success rate useful for making college football predictions. I summarize these in my ultimate guide to college football analytics, which also discusses the randomness of turnovers.

What statistics matter most in picking a Super Bowl champion?

As a Super Bowl winner, in order, rank the aspects of teams that seem most key in determining Super Bowl champions: Passing, Rushing, Yards of Total Offense, Turnover Margin, Average Field Position, Penalties, Yards Allowed by Defense, Defense vs Run, and Defense vs Pass?

— Yoni Aharon, Member.

To determine the team with the most likely chance to win the Super Bowl, you need to find the best team. Hence, I came up with these rankings.

  • 1. Passing, Defense vs Pass. Sometimes cliches are true. The NFL is a quarterback’s league. This also implies that pass defense is important.
  • 2. Turnover margin, Average Field Position. These are clearly important, but teams have little control over these numbers. There is a wealth of research on the randomness of turnovers, while Bob Stoll has discussed how special teams performance in the past has little ability to predict future performance in the NFL. (I think I heard this on a Beating the Book podcast.)
  • 3. Yards of Total Offense, Yards Allowed by Defense. These are important because they reflect strength in passing and pass defense. It would be better to look at yards per play, but most NFL teams play at roughly the same pace.
  • 4. Rushing, Defense vs Run. There is little correlation between rush efficiency and winning in the NFL. This doesn’t imply that rushing doesn’t matter. It just matters much less than passing, which is why running backs no longer get the monster contracts.

I honestly don’t know about penalties. I imagine they don’t matter much.

Do defensive shifts in baseball work?

My question involves positional shifts in baseball. You see almost every team employing them on a pretty regular basis nowadays.

Many times a batter will hit right into the shift but I have also seen many instances where a double play grounder rolls right through a vacated infield spot. Pitchers then get very angry!

Is there a way for you to determine the success rate of defensive shifting? On the surface I think shifts give up just as many hits as they take away but I would like to get your take.

— Jim Winter

The data suggest that shifts work. This article claims that shifts saved 390 runs for all major league teams in 2014.

However, I think there’s a ton of randomness in these numbers from season to season. The table in the previous article suggest the Astros were great at saving hits with shifts while the Rays and Pirates were not.

However, all three of those teams have sophisticated analytics operations. The Rays inspired the Pirates, and the Pirates suddenly had a great defense in 2013. Check out the details in this article by Travis Sawchik. (I apologize for the annoying, unstoppable video ad.)

The randomness in predicting NFL and NBA games

Year after year- why is NFL scoring so unpredictable from one week to the next throughout each season. Maybe you have already done work related to this question and if so could you please direct me to a link?

— Chris Guy

Is predicting outcomes ATS (against the spread) most challenging in the NBA vs all other sports?

— Scott Shoultz

Predicting outcomes in professional sports is hard.

For the 2014 NFL season, 21 of 32 teams had a rating within 5 points of the league average in my team rankings. For the 2014-15 NBA, my team rankings had 22 of 30 teams within 5 points of the mean rating of 0.

This means that small events can change the number of points scored and tip the results of games. A dropped touchdown pass in football or a lay up that rims out in basketball can turn a winning team into a loser.

What’s the toughest sport to predict against the spread? I would guess the NFL just because it gets the most attention. However, that doesn’t mean the NBA is easy to bet.

How to construct an NBA team based on chemistry

One thing I find myself wanting to read more about is the analytics behind constructing a team. In the NBA we know shots around the rim and corner threes are the most efficient shots, but are there specific metrics to assess the synergy among players when compiling a roster, or should we take each player at face value based on their individual stats?

— Christopher Saik.

Team chemistry is certainly a holy grail for analytics.

This article looks at two papers presented at the Sloan Sports Analytics Conference. Both papers seem interesting but not a huge break through.

You can also look at the plus minus for a group of players on the floor. The teams probably have this data, although I can’t find a public source.

Team synergy is a tough one to get at with numbers, and that might always be the case. Sometimes, you just have to watch the games.

Tracking The Power Rank’s accuracy

What’s your record for ncaa football and pro football for the past 5 years?

— Anthony Cristiani

A full answer to this question is coming soon. I’ll go back and look at how the predictions I’ve posted have done. I’ll also back test the model I’ll use for the upcoming season.

On the predictions page, I’ve done a better job tracking my baseball results. From May 29 to June 11, 2015, the team with the higher win probability has won 105 of 188 games for a win percentage of 55.9%.

Predictions for week 16 of the NFL, 2014

NFL_Rankings_Week_16Though it’s late in the season, Ed and I have teamed up to provide some NFL previews for the remaining games. I am the owner of where I provide analytical sports information and betting systems. I major in baseball but also cover the NFL and NHL.

In this segment, I will be providing a weekly view on some important games to watch from an analytical and betting perspective. You can follow along with me on Twitter @realFrankBrank and check out my betting systems and sports analytical models at

Eagles @ Redskins

Fresh off two losses and dropping from their top spot in the NFC East, the Eagles get the privilege of traveling to Washington and taking on the helpless Redskins.

The Redskins have only failed themselves. The owner has failed the coach who has failed the players who have failed each other.

On the other side, the Eagles first ran into a revived Seahawks defense two weeks ago. This past week, their defense got torn apart by Tony Romo and Dez Bryant while sprinkling in some Cole Beasley and Jason Witten.

It wasn’t a good look for this popular Eagles team. Their uptempo offense has struggled for long periods of time throughout the previous two games.

Mark Sanchez may have caught the Chip Kelly hype, but he hasn’t been much more than an average quarterback when you look at his 6.9 yards per pass attempt (includes sacks but not adjusted for competition) and 61% completion rate. Sure, the high pace offense and large amounts of points are great. The rate stats tell a different story.

The defense, particularly the secondary, hasn’t played well all year, ranking 18th in adjusted yards per attempt against. The largest edge the Eagles will have against the Redskins is their second-best 8.4% sack rate.

Washington QB Robert Griffin has struggled to elude pressure. He looks slower, indecisive, and has been sacked twenty-eight times in seven games (five starts) for a ridiculous 15.7% of drop backs. That’s about one sack for every six drop backs for RG3. Sacks play a huge factor in the outcomes in NFL games and a 15.7% sack rate is impossible to overcome no matter the quarterback.

However, when Griffin is able to throw the ball, he’s performed at a high level by completing a 69% of his passes with a mere 2% interception rate. This Eagles secondary may be just what Griffin needs to have a decent game. I don’t think Washington can win this game. I do think this 8.5 point line is too much and the game will play much closer. (Ed note: The line is now 7.5 in most places.)

The typical three point edge to home teams suggests this game would be lined at 14.5 (or more depending on where this line finishes) if it were played in Philadelphia. That’s too many points to bet on for a bad defense. The Power Rank’s team rankings based on margin of victory says Eagles by seven, and I can agree with that. When this line finishes at or over ten points, it’ll be worth grabbing the Redskins.

Chiefs @ Steelers

The Steelers are the hardest team to project this season. They’ve blown out some of the better NFL teams like the Colts, Ravens, and Bengals. They’ve also lost to the Jets, Bucs, and Saints.

The Chiefs aren’t too much different. They’ve beaten the Patriots, Chargers, and Seahawks while losing to the Raiders, 49ers, and Titans. This type of variance from game to make makes projecting teams difficult.

There’s one thing easy to find in this one: the Steelers play great at home. To go along with that, the Chiefs haven’t been great on the road. Most recently, they’ve lost to the not-so-good Cardinals, the Raiders, and skated by the Bills in a game in which they lost in every aspect besides the final score.

A quick look at the Steelers home scoring tells the whole story. They’ve scored 30, 24, 30, 51, 43, and 32 points at home for an average of 35 points. There aren’t many defenses that can stop them from scoring, particularly at home.

The Chiefs, who still don’t have a touchdown pass to a wide receiver this season, seem to be the exact opposite. They have a pedestrian, risk adverse offense that tries to win on defense and check downs to running backs and tight ends.

No matter who Dick LeBeau is running out on defense, he can game plan against this offense to keep everything in front of them. The Steelers losses have come from the secondary getting exposed, putting them into a hole early. They give up an average of 12.7 yards per catch and 7.3 adjusted yards per pass attempt, third worst in the NFL.

However, the Chiefs offense can’t expose this Steelers weakness. The Chiefs offense gains just 10.9 yards per catch, ahead of teams like the Jets, Bills, Jaguars, and Vikings.

I expect the Steelers offense to get a lot of possessions and score a lot of points again. The Chiefs can’t keep up in this one.

The betting line is at three, which basically says these two teams are equal on a neutral field. When I initially saw the line, I expected a ton of Steelers backers. However, 60% of the public has come in on the Chiefs so far.

The Power Rank has picked the Chiefs to win. I just see match up problems for the Chiefs this week. Pittsburgh wins big.

Falcons @ Saints

The NFC South is putrid. It’s a shame, really, that one of these teams will make the playoffs.

After Monday night’s blowout of the Bears, the Saints have taken the lead in the division with a 6-8 record. One of the Seahawks, Lions, Packers, Eagles, or Cowboys aren’t going to make the playoffs because the Panthers, Falcons, or Saints are guaranteed a playoff spot.

Let’s hope the Saints, the best NFC South team by The Power Rank’s ensemble rankings, can win out to get to 8-8. That may be harder than it seems.

The Saints usually have a large home field advantage, but that has not been the case this season. They’ve been blown out by marginal teams like Carolina and Cincinnati while also losing to Baltimore and San Francisco.

Both of these teams have had issues stopping opposing quarterbacks, and I expect the same this week. New Orleans and Atlanta have the 29th and 32nd ranked pass defense by yards per play adjusted for schedule respectively. These pass defenses will struggle against Drew Brees and Matt Ryan, who both lead above average pass offenses.

A standard three point edge to the home team for an evenly matched game gives the Saints the edge by a field goal. When you consider how great the Saints have been at home, you could probably assume a 4.5 to 5 point edge. Interestingly, The Power Rank gives the Saints exactly that, a win by 4.8 points.

Vegas has booked this line at Saints -6.5. With a strong performance on prime time Monday Night Football, I’d expect the fan favorite Saints to steal a majority of the public bets.

Colts @ Cowboys

This is the game of the week. Coming off four straight prime time games, the Cowboys need to win out to secure a playoff spot. There are other ways to get in, but they would need some external help from some bad teams. Considering the Cowboys play the Redskins next week, this game against the Colts could determine their playoff fate.

The Cowboys are small favorites in this game but betting lines can be misleading. There’s not a more polarizing team in the NFL when it comes to public perception than the Dallas Cowboys.

A sportsbook’s lines are hypothetically made so that game ends up on either side of the line 50% of the time. In actuality, the lines are made to persuade the public to bet 50-50 to reduce risk in the books losing out. With the Cowboys coming off a big prime time win and all things being equal, one could assume Cowboys -3 is a bit of an inflated line.

The Cowboys have been a bad home team. Most dome teams have a sizable advantage at home (see Saints, Colts, Falcons, etc.). It has an opposite effect for Dallas.

Dez Bryant is obviously a huge threat to any defense, but their strength is dominating with the offensive line and running the ball with DeMarco Murray. Now that Murray has had surgery on his broken hand, he could see a reduced role or maybe even sit this one out. I don’t see any issue with Joseph Randle and Lance Dunbar being able to pick up the slack behind this offensive line.

It’s no secret this Cowboys defense, especially the secondary, is bad. Are they better than we thought they’d be? Definitely. However, they rank 28th in adjusted yards per pass attempt.

The Colts also have looked pretty vulnerable the last two weeks. Andrew Luck has surrendered a few touchdowns to opposing defenses while barely sneaking by the quarterback-troubled Browns and Texans. They also haven’t been a great road team. They’ve held serve to this point by skimming past the Browns in Cleveland and Texans in Houston. However, Indianapolis went down big in Denver and was blown out in Pittsburgh.

Both of these defenses has their shortcomings while both offenses can really move the ball. The Power Rank has this one as a virtual tie (Cowboys by 0.5). I can agree with that. The Colts struggle on the road, the Cowboys struggle at home.

It looks like last possession wins with two lights out kickers. Don’t forget about the kickers!

Additional leans:

Jaguars -3, Lions -5, Panthers -3.5, Bengals +3.5

Thanks for reading. You can follow me on Twitter @realFrankBrank and check out my betting systems and sports analytical models at

How to predict interceptions in the NFL, backed by surprising science

photoTurnovers play a critical role in football.

A tipped pass for an interception or crushing hit for a fumble can decide a close game. No coach emerges from a press conference without touting the importance of winning the turnover battle.

However, not all turnovers are created equal. In the 2013 NFL regular season, teams with more interceptions than their opponent won the game 80% of the time. Teams that forced and recovered more fumbles than their opponents won the game 70% of the time.

Interceptions have a bigger impact because the defender is most likely on his feet after the takeaway. This can lead to a big swing in field position or even a score. Defenders that recover fumbles tend to fall on the ball.

What factors affect interceptions in the NFL? Here, we’ll look at the surprising analytics behind interceptions.

You can do better than guessing that each team will throw picks on 2.9% of pass attempts, the NFL average. And it doesn’t involve an arcane statistic that comes from charting games. The critical numbers are in the box score, although it might not be the numbers you expect.

We’ll also look at how this analysis changes the predicted point spread for a game.

How pass rush affects interceptions

Seattle cornerback Richard Sherman led the NFL in interceptions in 2013. Despite all of his public claims about being the best cornerback in the league, Sherman credits his front seven for much of his success.

Pass rush is an obvious candidate to affect interceptions. The more often a defense applies pressure on the quarterback, the more often he throws an errant pass. Or perhaps the defender strikes the quarterback’s arm, causing a wobbly pass to fall into the hands of the defense.

To study this, we need to measure the strength of the pass rush. To start, let’s look at sacks, a number that requires proper context. A defense might rack up more sacks by facing more pass attempts. To account for this, let’s use sack rate, or sacks divided by the sum of pass attempts and sacks, as a measure of pass rush.

To determine whether pass rush causes interceptions, consider NFL defenses in the regular season from 2003 through 2013. While I expected defenses with a better sack rate to have a higher interception rate, there’s no correlation between these two quantities for these 352 defenses.

For those with a technical inclination, sack rate explains less than 1% of the variance in interception rate. For everyone else, check out the left panel of the visual in the next section.

Richard Sherman might be a great cornerback because of Seattle’s pass rush. However, his pass rush doesn’t explain his high interception total in 2013.

How pass protection affects interceptions

If pass rush has no effect on a defense’s interceptions, what about pass protection on offense? An offensive line that keeps pass rushers away from the quarterback might result in fewer interceptions.

Over the same 11 regular seasons, the sack rate allowed by an offense explains 6% of the variance in the interception rate. While this correlation is stronger than on defense, I still do not recommend using sacks to predict interceptions. The right panel of the visual shows why.


We can dig even deeper into pass protection. Over the last 5 seasons, the NFL has tracked QB hits, or the number of times the quarterback gets hit after releasing the ball. We can now calculate the rate at which an offensive line allows the hits on the quarterback (the sum of QB hits and sacks divided by the sum of pass attempts and sacks).

This QB hit rate gives a better perspective on pass protection. An offensive line might look good because of a low sack rate. For example, Indianapolis gave up sacks on 5.2% of pass attempts in 2013, 5th best in the NFL.

However, this same offensive line allowed a hit rate of 23%, 26th worst in the NFL. Andrew Luck’s ability to get rid of the ball in the face of pressure played a big role in their low sack rate. The lack of protection probably also contributed to Luck’s below average completion percentage of 60% in 2013.

However, even a better statistic like QB hit rate doesn’t correlate with interceptions. Hit rate explains 4% of the variance in interception rate, a weaker correlation than shown in the right panel of the visual.

The data does not support the belief that pass rush affects interceptions. I would guess this comes from the ability of NFL quarterbacks to not let pressure to affect their accuracy. Of the thousands that play in high school and hundreds that make it to college, only 32 can play in the pros. These quarterbacks do not fold under pressure.

However, these 32 quarterback do vary in their accuracy, and that might impact interceptions.

How throwing accuracy affects interceptions

Despite the wobbles of the his balls, Peyton Manning has shown incredible precision with his throws. Over his career, he has completed 65.5% of his passes. Of active players, only Drew Brees and Aaron Rodgers have a better career completion percentage.

However, Peyton has gotten even better after having multiple neck surgeries. In his last two seasons with Denver, his completion percentage has increased to 68.4%.

Do more accurate quarterbacks throw fewer interceptions? Any fan would rather have Manning and Rodgers leading their offense than Derek Anderson or Brady Quinn. But are fewer interceptions a consequence of a better quarterback?

To answer this question, consider the career statistics for NFL quarterbacks in 2013 with at least 500 career pass attempts. The visual of these 52 players shows the negative correlation between completion percentage and interception rate.


Peyton Manning is the third point from the right, and he has thrown picks at a higher rate than this regression analysis predicts. Aaron Rodgers has the lowest interception rate of the 3 quarterbacks with better than 65% completion rate.

The outlier with the lowest interception rate is Nick Foles, the second year quarterback with Philadelphia. As much potential as he has shown, he will not continue to throw interceptions on 1.2% of his pass attempts. The same applies to San Francisco’s Colin Kaepernick, the point with the second lowest interception rate (1.7%).

This correlation does not imply that better accuracy causes fewer interceptions. But this conclusion does seem logical. The quarterback has control over where he throws the ball. The more control he shows, the less likely the ball hits the hands of a defender. There are better ways to look at this causation, but they will have to wait for another day.

For these quarterbacks, completion percentage explains 32% of the variance in interception rate. In the noisy world of football statistics, that’s as strong a relationship as you will see between two statistics. In addition, the correlation also exists for the regular season statistics of offenses from 2003 through 2013. Here, completion percentage explains 20% of the variance in interception rate.

With this strong relationship between accuracy and interceptions, how can we modify a point spread prediction for a game?

How interceptions affect the point spread

To use these results to adjust a prediction, let’s look back at the Super Bowl between Seattle and Denver at the end of the 2013 season. Before the game, the team rankings at The Power Rank predicted Seattle by 1.3 points, which implied a 46% chance for Denver to win.

Denver had a lower likelihood to throw a pick based on Peyton Manning’s accuracy. On average, NFL quarterbacks throw interceptions on 2.9% of pass attempts. With Peyton’s 65.5% career completion percentage, the regression model predicted he would throw interceptions on 2.56% of pass attempts. For a league average 35 pass attempts, this meant 0.14 fewer interceptions for the game.

While such a small fraction of picks might seem inconsequential, the impact of such a turnover makes it matter. From the relationship between interceptions and points in NFL games, the average interception is worth about 5 points. This changed the predicted point spread by 0.7 points in Denver’s favor. Seattle’s predicted margin of victory dropped from 1.3 to 0.6, which increased Denver’s win probably from 46% to 48%.

The game didn’t go Denver’s way. Seattle’s defenders knew what mouthwash Manning used before the game since they spent the entire game in the backfield.

The outcome of interceptions in the Super Bowl

Manning thew 2 interceptions. The first was an errant pass that landed in the hands of Cam Chancellor, a play in which Manning wasn’t pressured that heavily. The second pick came when a defender hit his arm on a throw. The football wobbled into the hands of Malcolm Smith, who ran for a Seattle touchdown.

For the game, Manning thew 49 passes, so the two picks implied a 4.1% interception rate. Even with this small sample size, that is not an outrageous rate. If not for the bad luck on the pick in which his arm got hit, Manning would have had a 2% rate.

Common sense says that pass rush and throwing accuracy affect interceptions. However, the NFL data only shows a link with one of these factors. If you want to predict interceptions, stay away from pass rush statistics and look at completion percentage.