THE POWER RANK

Podcast: Ed Feng on How to Win Your March Madness Pool

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I got to be the guest on my own podcast! My name is Ed Feng, founder of The Power Rank and Stanford Ph.D.

Edward Egros interviewed me on The Football Analytics Show, and we discussed:

  • How my predictive analytics have changed during the pandemic (2:02)
  • How I got into sports analytics, and why I wouldn’t recommend this approach to others (7:32)
  • How I’ve gotten lucky with my bracket advice over the years (13:03)
  • The importance of watching games (17:55)
  • Why you need to avoid information overload (22:58)
  • Contrarian strategies for winning your March Madness pool (26:56)
  • The impact of the location of the 2021 NCAA Tournament (33:17)
  • Whether the ending of basketball games should change (39:21)

To listen here, click on the right pointing triangle.

To listen on Apple Podcasts, click here.

How sports analytics helped Michigan basketball in way so obvious even Ohio State fans noticed

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Why should you believe in sports analytics? Have numbers ever helped a team?

Predictive analytics can help you win your March Madness pool, but how about a real example in which numbers helped a team? An example so obvious that even a team’s most bitter rival can see the change?

I stumbled across a story this fall.

Tracking technology on Derrick Walton

In November of 2017, Michigan basketball coach John Beilein spoke at the Exercise and Sports Science Initiative (ESSI) Symposium at the University of Michigan. He talked about how his team uses technology from Catapult to track the work rate of players.

During the 2016-2017 season, he noticed point guard Derrick Walton was working harder than his teammates the day before games. If Catapult quantified the work rate of others at 800, then Walton measured at 1000.

Beilein didn’t think this 25% extra work was necessary, so he asked Walton to tone it down the day before games. After his talk, I raised my hand to ask whether he made this change in January.

To understand why I asked about this specific time, let’s go back to Michigan’s 2016-2017 season.

Michigan’s struggles in 2016

Michigan struggled during the first part of conference play in January of 2017. They started 1-3 against Big Ten competition and looked terrible.

Derrick Walton was a part of the problem, as he wasn’t playing well. We’ll get into specifics soon.

But first, let’s look back on why many Michigan fans liked Walton. He started as a freshman on a Michigan team that made the Elite Eight. He got injured his sophomore year, but his three point shooting and ability to get to the basket seemed to projected a bright future.

During the start of his junior year, I tweeted this.

More than a year later, this tweet didn’t seem insightful as Walton struggled into his senior season. He looked like an average Big Ten point guard, and I gave up on him as a game changing player.

I remember asking Sam Webb about Walton, as the point guard seemed like a hard worker destined for success. Webb, the ultimate Michigan football and basketball insider, confirmed his work ethic but also wondered why Walton wasn’t playing better.

Then, starting in January of 2017, Walton goes on a tear.

A tale of two seasons

On January 14, 2017, Michigan played Nebraska. Before this game, Walton converted 38% of his two point shots. Starting with Nebraska, he made 49% of his two point shots.

For a guard like Walton, two pointers represent high energy shots like lay ups or a pull up jumpers. More energy during games could contribute to this increase in field goal percentage.

Randomness plays a bigger role in 3 point jumpers, but Walton also improved in this area. He hit 40% of his 3 pointers before Nebraska, but then 44% after.

Known as a great rebounder, Walton’s rebounding improved as well. His rebounds per minute increased 25% starting with the Nebraska game compared to before that game.

Michigan played better basketball starting with Nebraska. They ended the season 9-5 against Big Ten competition. However, they still needed to win games in the Big Ten tournament to make the NCAA tournament.

Then, the Michigan basketball team almost died.

Post season play for Michigan

As they tried to fly to Washington DC for the Big Ten tournament, strong winds swept their plane off the runway. Luckily, no one was seriously hurt.

The experience shook Michigan to a higher level. They won the Big Ten tournament, as Derrick Walton took home Most Valuable Player honors.

Michigan now had an automatic berth into the NCAA tournament, and they beat Oklahoma State in a close opening round game. Then, as a 7 seed, Michigan beat 2 seed Louisville in their best game of the season to make the Sweet 16.

Their Sweet 16 game against Oregon went down to the wire. Down 1 point in the waning seconds of regulation, Walton had a pull up jumper from about 15 feet that would have won the game.

In the fairy tale, the shot drops and Michigan advances to the Elite Eight. In reality, the shot bounced off the rim.

No matter the result, Derrick Walton’s play led Michigan on a remarkable run to close the 2018 season. He now plays for the Miami Heat.

Walton’s run started in January, so it prompted me to ask coach Beilein whether he made Walton tone it down in practice around then because of the player tracking data.

Yes, said Beilein.

Data. Analytics. It works.

Cover image courtesy of Marc-Gregor.

NCAA is meeting with quants to make tournament selection process better

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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

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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

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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.