NCAA is meeting with quants to make tournament selection process better

On Friday, January 20th, 2017, hell will freeze over, and the NCAA will meet with analytics guys like Ben Alamar, Jeff Sagarin and Ken Pomeroy. The conversation will revolve around making the tournament selection process better.

You can read about it here, but two points stand out for me.

First, they say the following about the RPI rankings the committee currently uses.

An even more powerful microscope to go with the time-honored RPI.

Time-honored my ass. The RPI is stupid for two reasons:

  • It lacks a solid mathematical basis (compare it with the least squares rankings that Pomeroy uses)
  • It uses wins and losses instead of margin of victory in its calculations

I discuss both of these issues in relation to college football here. Hence, RPI fails as a predictor for how teams fare in the tournament.

The NCAA should eliminate RPI from the selection process.

Second, Jim Schaus, the athletic director at Ohio State and committee member, said this:

I’m going to have to strap on in the meetings to stay up with all the calculus that’s going to be discussed, but I’m excited about it.

Calculus is so overrated in our society.

You want to hang with the quants, Schaus? Then let’s talk probability, or that no analytics ever says a team will beat another team with 100% certainty.

Want to get fancy, Schaus? Then let’s dig into linear algebra so you can understand the least squares method used in adjusting for strength of schedule.

I’m all for learning calculus. It’s just not as useful in sports analytics as probability and linear algebra.

Podcast: Ben Alamar on the ESPN Football Power Index and NFL Divisional Playoffs

On this week’s episode of the Football Analytics Show, I’m honored to have Dr. Ben Alamar, Director of Sports Analytics at ESPN. We dig into a host of topics, which includes:

  • The NBA executive that opened the conversation with “I don’t believe this analytics stuff”
  • How FPI (Football Power Index) makes adjustments for quarterback injuries
  • The surprising team to which FPI assigns the second highest Super Bowl win probability
  • The hidden factor that gives Dallas an extra point in the FPI prediction against Green Bay
  • Spread predictions for all 4 Divisional Playoff games for FPI (not available anywhere on the internet to my knowledge) and my member numbers
  • Ben’s 3 tips for breaking into sports analytics

You can check out the work of ESPN analytics team by clicking here.

To listen to the show on iTunes, click here.

To listen here, click on the play button:

Podcast: Cade Massey on the NFL Playoffs and College Football Championship Game

On this week’s episode of the Football Analytics Show, I’m joined by Cade Massey, professor at the Wharton School at the University of Pennsylvania. He studies judgment under uncertainty, and there’s no better example than his Massey-Peabody football predictions.

We cover a wide range of topics, which includes:

  • How Cade has learned humility in building a predictive football model
  • The playoff karma of the New York Giants
  • What data says about whether match ups matter in football predictions
  • The sneaky trick for breaking into the sports analytics world
  • The Massey-Peabody prediction for Alabama versus Clemson

For match ups, I discuss a similar study in college basketball.

To listen on iTunes, click here.

To listen here, click on the play button.

Appearance on Digital Entrepreneur podcast

screen-shot-2016-11-08-at-12-38-01-pmWay back in the day, I had no idea how to run an online business. Then I found this site called Copyblogger, and their idea of content marketing made a whole lot of sense to me.

I’ve learned just about everything I know about running an online business from the good folks at Copyblogger (now rebranded as Rainmaker Digital). So it was a real honor to appear on their Digital Entrepreneur podcast with Jerod Morris.

If you’re interested in how I’ve grown The Power Rank as a business, 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%.