Study shows interim coaches have little impact on bowl games

Screen Shot 2015-12-18 at 11.00.21 AMIn an excellent article over on Sports on Earth, Ross Benes used my college football team rankings to study the impact of interim coaches on bowl games.

He looked at all bowl games since the 2005 season and split the 42 games with an interim coach from the remaining 295 games. He found the statistics of both groups to be roughly the same.

The Power Rank actually performed better in predicting the results of games with an interim coach (71% compared with 59% in games without an interim coach). However, this is mostly likely a fluke due to small sample size.

The study does suggests you can ignore coaching changes when predicting the winner of bowl games.

To read the article, click here.

The Power Rank 2015 Bowl Season Cheat Sheet

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If you’re entering a bowl pool, you might be interested in the 2015 bowl cheat sheet.

The study by Benes uses my calculations based on points, or margin of victory adjusted for strength of schedule. In the cheat sheet, I combine these predictions with others based on yards per play. The larger ensemble of predictors makes for stronger predictions.

These prediction picked the winner in 76.5% of college football games this season. In addition, I expect the results of the study to apply to these predictions: ignore the impact of interim coaches.

For example, suppose you’re trying to pick the winner of Georgia versus Penn State. Georgia fired Mark Richt and installed wide receiver coach Bryan McClendon as interim coach. Georgia also won’t have the services of either coordinator for the bowl game.

It most likely doesn’t matter. My bowl cheat sheet predicts Georgia by 8.6 points over Penn State, which corresponds to a 74% win probability. Go with the Bulldogs.

To get the 2015 Bowl Season Cheat Sheet, click here.

The accuracy of The Power Rank’s 2014 college football predictions

How did my college football predictions do in 2014? Here, I look at not only my posted numbers for all games but also the forecasts made on this site and other outlets such as Grantland, Deadspin and Bleacher Report.

It’s 2015, and I’m making a full effort to track and report on all of my predictions. It started with baseball this spring, and it will continue through football and basketball.

Let’s get started.

Best prediction

Before the BCS title game, I wrote on Deadspin about how Ohio State presented a terrible match up for favorite Oregon. Ohio State had a vicious rushing attack that had just mauled a strong Alabama defense. Oregon had an average rush defense.

During the game, Ohio State RB Ezekiel Elliott gashed Oregon for 246 yards on 36 carries (6.8 yards per carry). Despite 4 turnovers, Ohio State won 42-20.

In the comments of the Deadspin article, a reader wrote this:

It is the start of the fourth, and it is creepy how on point your predictions are.

Two of my preseason predictions make honorable mention.

Auburn to regress

Auburn had a dream season in 2013, as they rose from the ashes of the SEC West to win the conference and play in the BCS championship game. However, they got the benefit of a few lucky plays (a tipped hail mary completion against Georgia, a field goal returned for a touchdown against Alabama).

In August, I wrote about how Auburn would have a tough 2014 season because of their schedule and the small chance they benefit from those lucky type plays again. Auburn fans didn’t like that I called them lucky.

Auburn couldn’t reproduce those plays in 2014. After a magical 12-2 season in 2013, they fell to 8-5 last season. Part of their demise was a tough cross division game at Georgia that they lost.

TCU to win the Big 12

My other favorite preseason prediction was that TCU would win the Big 12.

I actually went against my numbers on this one as Oklahoma had a higher win probability. However, no one gave TCU a chance, and I had them ranked 14th in the preseason.

TCU had a tremendous season as they went 12-1 and finished as co-champions with Baylor of the Big 12.

Worst prediction

Grantland asked me to predict the Heisman winner during the preseason. I don’t make player predictions, so I had some fun and picked Stanford QB Kevin Hogan.

I thought I had some strong reasoning, but Hogan came no where near the Heisman conversation. He led a Stanford offense that made red zone stalls a season long habit. This led to a disappointing 8-5 season.

Halfway through the season, Grantland gave me a do over and asked for another Heisman prediction. This time I go with Bo Wallace, the quarterback of a 6-0 Ole Miss team. Part of my reasoning was his improved completion percentage the first half of the season, an oh so huge sample size to make a judgment.

I wrote the following about my Wallace pick.

It’s hard to deny a blond quarterback from an unexpected SEC contender.

Then Wallace had a terrible second half of the season. He couldn’t make a play in a close game against LSU, and Ole Miss lost their first game of the season. The once mighty Rebels lose two more SEC games before getting blown out by TCU 42-3 in a bowl game.

Don’t ask me about the Heisman.

The Ohio State season end surge

For predictions based on my numbers, I was disappointed to not predict Ohio State’s surge at the end of the season. Before the Big Ten title game, they were 13th in my team rankings. Their loss to a bad Virginia Tech team at home pulled them down.

Then Ohio State plays the 3 best games any college football team has ever put together. They become the first national champion in the playoff era.

Member predictions

Members get access to my best predictions for spreads and totals.

The member predictions with the most value are the college football totals, which were posted from week 6 to the end of the season in 2014. These predictions went 53.3% against opening totals (273-239-4) and 51.5% against closing totals (260-245-9).

However, these numbers do not tell the entire story. When the predicted total differed from the opening total by more than 4 points, the market total moved in the direction of the prediction 90.4% of the time (122-13, with two totals that didn’t move).

On average, the final total moved 3.5 points in the direction of the model prediction. Some refer to this as closing line value.

For the entire 2014 season, spread predictions for members were 50.1% against the opening line (367-366-19) and 48.6% against the closing line (357-378-8). Modifications will be made to this model for 2015.

To learn more about becoming a member of The Power Rank, click here.

Public predictions

On the predictions page, I posted a margin of victory for each college football game.

These predictions got the game winner correct in 70.4% of games (539-227). It’s interesting that my preseason model, which doesn’t use data from the regular season, predicted a higher percentage of game winners (71.1% on 482-196 with no predictions on the other games).

Against the markets, the public predictions won at 49.9% against the opening line (370-371 with 18 pushes) and 48.5% against the closing line (363-385 with 9 pushes). It’s tough to beat the markets on every game.

The public predictions will be reworked this season. There’s room for great improvement, especially since these predictions were 53.8% against the opening spread through week 8 of the season.

Playoff probabilities on Bleacher Report

Playoff predictions on 11-18-2014.

On Bleacher Report, I predicted which teams would make the four team playoff based on the committee rankings. To learn more about these simulation methods, click here.

Overall, I thought the predictions did pretty well. Mississippi State was first in the committee’s first rankings. However, my numbers thought they wouldn’t make it due to tough games at Alabama and Ole Miss. Mississippi State lost both games and didn’t make the playoff.

By week 12 of the season, Alabama, Oregon and Florida State had the highest chance to make the playoffs by my calculations. Eventually, all 3 of these teams made the playoff.

However, the predictions were off the last week of the season as my numbers had TCU instead of Ohio State for the last spot. As I mentioned earlier, it was difficult to predict Ohio State’s surge at the end of the season based on their previous numbers.

However, my methods also need work. I had no way of knowing how the committee would value a conference championship. For 2015, I’ll account for this in my model.

Even with this improvement, there are still human factors out of my control. Though the committee placed an emphasis on a conference championship, Big 12 commissioner Bob Bowlsby still presented them with co-champions in Baylor and TCU. It might make more sense to crown Baylor the champion as they beat TCU.

My other decent predictions

Some of my other predictions had the right idea but didn’t nail it.

Thanks for reading.

Accuracy of The Power Rank’s baseball predictions in 2015

worn-out_baseballOn April 23rd, 2015, I started posting a win probability for every MLB game. These predictions had two components:

From April 23rd to May 28th, 2015, the team with the higher win probability won 252 of 487 games for win percentage of 51.7%. The predictions did well for awhile, but they got weaker as the season progressed.

On May 29th, I changed the methodology behind the baseball predictions. Instead of actual run differential as an input to my ranking algorithm, I used an expected run differential according to the Base Runs formula.

For every team that hits 115 doubles and 58 home runs at this point in the season, some score 330 runs while others, like the Detroit Tigers, score 293. The higher scoring teams tend to cluster their hits due to better clutch hitting.

However, my research shows that good and bad cluster luck is not sustainable. Teams with extremes in cluster luck tend to regress to the level of run production given by the Base Runs formula.

With these new team rankings based on expected runs, the predictions have performed much better. From May 29th through June 21st, the team with the higher win probability has won 187 of 335 games for a win percentage of 55.8%.

For comparison, the team favored in the betting markets has won 181 of 335 games for a win percentage of 54.0%. The markets get half a win for offering the same odds on both teams.

The markets will catch up soon. They always do. However, this is a pretty accuracy for predictions that do not account for injuries.

Check the predictions page for daily updates to this record.