Category Archives: Prediction

Sour grapes

True Wins did okay over the weekend — predicting the 49ers and Patriots victories, but whiffing on the Ravens and Falcons. Picking the team with more True Wins so far has six correct and two wrong, while relying on actual wins to pick has just four right, two wrong, and makes no prediction on two games (same records for Pats-Texans and 49ers-Packers).

The True Wins king — Denver — is out! Over the last ten years, the top team in True Wins has won four Super Bowls, and the second ranked team has won two more. It’s up to the Patriots (#2) to carry on the tradition, even though the 13.5 True Win Broncos had the second highest total in the last ten years (behind another famous losing team that you may remember). That couldn’t save them from one very cold Manning flinching first in a stalemate and one very cold Champ Bailey getting toasted over and over again (not to mention one very cold referee blowing a couple video reviews and throwing a ton of flags on the Broncos). More on the playoffs later in the week, but for now, I want to go back to some old predictions and talk about this year’s playoff spectators.

The Sour Grapes Club

Last year, I broke the outsiders-looking-in into four groups (follow the link — the predictions are worth a read-through in their entirety):

  • The Michael Vick Division (pretty good teams that had some bad luck): Eagles, Bears, Chargers, Cowboys
  • The Cam Newton Division (mediocre teams with something to build on): Panthers, Titans, Seahawks, Dolphins, Vikings
  • The Rex Ryan Division (overconfident teams that need to reassess their approach): Jets, Cardinals, Bills, Raiders, Chiefs
  • The Sam Bradford Division (teams that need to start over completely): Redskins, Jaguars, Browns, Colts, Rams, Buccaneers

The first thing that has to change are the names. Cam Newton moves up a notch and replaces Vick, who unfortunately goes all the way around the horn to replace Bradford in the blow-it-up division. Rex Ryan, one of the most overconfident men in the world, is saved by Tony Romo and his buddy Troy Aikman — I can’t listen to Aikman defend Romo anymore. I’m sticking with my man Stafford and handing the “something to build on” division to him, even if my Lions regressed this year.

Here are the standings this year:

2012 non playoff standings

From the first group (I expected good things): Continue reading

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Some questions and some predictions

Predictions

If you read my post last week, you know that the AFC is a two-horse race and the NFC is a mess. All four first-round games agreed with the True Wins predictions. I didn’t trust the Seahawks on the road, but True Wins came through (11 for the Seahawks versus just 9.5 for the Skins). So, what are we left with? Two clear favorites in the AFC (Patriots and Broncos) and two toss ups in the NFC. True Wins alone takes 49ers over Packers (11.5 to 11) and Seahawks over Falcons (11 to 10.5). I’m going to stick with the home teams in both cases, but don’t expect blowouts in the NFC unless the turnover margin is really skewed.

Questions

As part of football month on the blog, here are a couple random questions and answers that I’ve accumulated.

Should the NFL eliminate kickoffs? Greg Schiano, the Buccaneers crazy coach, thinks the NFL should get rid of kickoffs to protect player safety. A Rutgers player was paralyzed running kick coverage while Schiano coached there, so he knows exactly how dangerous kickoffs can be. Never mind that this is the same coach who runs a “kneel down blitz” when the other team is trying to kill the clock, a tactic that might work once when the other team is not expecting it, but will probably never work again.

Continue reading

Playoff Appetizer: True Wins Plus (Fumble Adjusted)

We might be halfway through the first quarter of the first NFL playoff game of 2013, but I’m still finishing up with baseball and just getting warmed up on football. Football month on the blog officially kicks off today — there’s lots of interest stuff to come, from innovative rule ideas and play calling to new prediction methods and game analysis. Today, I’m trying an addition to the measure of NFL team quality that I debuted last year: True Wins. True Wins are calculated as follows:

True Win = Blowout Wins + Close Wins/2 + Close Losses/2 + Ties/2

You may recognize the intuition from pythagorean expectations — you get full credit for blowout wins (I define this as more than 7 points), but no extra credit for winning by huge margins, and you get half credit for all close games, since those probably come down to luck more than skill. Last year, I showed that True Wins predicts a little better than pythagoreans, and it’s a whole lot more direct. Both measures are much better than using wins alone, which unfairly penalize (reward) teams that lose (win) a lot of close games.

What Else is Luck-Driven? Fumble Recoveries?

With the playoffs coming right up, I decided to try an improvement that adjusts for possible luck in fumble recoveries as well. Here’s the logic (from Football Outsiders):

Stripping the ball is a skill. Holding onto the ball is a skill. Pouncing on the ball as it is bouncing all over the place is not a skill. There is no correlation whatsoever between the percentage of fumbles recovered by a team in one year and the percentage they recover in the next year. The odds of recovery are based solely on the type of play involved, not the teams or any of their players . . . Fumble recovery is a major reason why the general public overestimates or underestimates certain teams. Fumbles are huge, turning-point plays that dramatically impact wins and losses in the past, while fumble recovery percentage says absolutely nothing about a team’s chances of winning games in the future. With this in mind, Football Outsiders stats treat all fumbles as equal, penalizing them based on the likelihood of each type of fumble (run, pass, sack, etc.) being recovered by the defense.

The keys are:

  1. Fumbles are huge turning points in games
  2. Teams don’t maintain high or low recovery rates over time

To quantify #1, I determined the point value of a recovery. A simple regression of point differential in each game on total fumbles and fumbles Continue reading

Is that a shiny new free agent in your stocking, or an old lump of coal?

NFL playoffs are right around the corner, but ’tis the season for a jolt of baseball excitement too, as teams sign new players. The contracts are getting bigger and bigger, supported by growing MLB revenues. Some of the major signings under the tree this year (more here):

  • Zack Greinke, 6 yrs, $147 million (Dodgers)
  • Josh Hamilton, 5 yrs, $125 million (Angels)
  • B.J. Upton, 5 yrs, $75 million (Rays)
  • Anibal Sanchez, 5 yrs, $80 million (Tigers)

But before you start thinking playoffs, remember that many big deals don’t work out. Who will be nice and who will be naughty this year?

The Old Lumps of Coal

From the list above, Greinke is 29 years old, Hamilton is 31, Upton is 28, and Sanchez is 28. Not many young players are available through free agency, but are these 4 to 6 year deals for 28 to 31 year olds a good idea? I tackled this question with my friend Jeff Phillips for ESPN the Magazine in early October.

Specifically, we wondered if long deals for 30 year olds made more sense during the steroid era, when players could recover, train, and maintain more easily. There are two sides of the coin: (1) how has older player performance changed, and (2) has older player compensation evolved appropriately. We focused on players in the top quarter of the salary distribution, since that’s where the big money is spent. To measure performance, we examined average Wins Above Replacement Player (WARP)* by age during and after the steroid era:

WARP bars

Uh oh. Although performance for all highly paid players has gone down, older “stars” have turned out to be coal indeed. Looking year by year highlights the post-PED age decline. Average WARP for older and younger stars was remarkably similar through the steroid era, but older player WARP Continue reading

The final tally

The tournament is over, and Kentucky are the champs. Who predicted that? Well, lots of people. Among the rankings I tracked, however (including my game simulations), only the tournament committee got it right by making them the overall number one seed. Here’s how the initial brackets fared for each ranking:

My simulations (final row) got off to a strong start through the first four rounds, as did the other quantitative approaches (Pomeroy and Sagarin rankings). However, seeding finished strong. By percent correct, the quant methods were slightly better (65 to 66% correct), but choosing based on seeding would have attained Continue reading

Predictions for tonight

The prediction for tonight: Kentucky over Kansas (54.6% of my simulations), but it should be close again (closer than the 6 to 6.5 point Vegas line). The simulations predict that the two teams are very close on most statistics, but Kentucky should rebound and shoot free throws slightly better and turn the ball over slightly less. We will see in 15 minutes!

The final four went almost exactly as my simulation predicted. Kentucky, who I predicted to win by 8.4, won by 8 points. Louisville did well on the offensive glass and limited their turnovers, but they fired up many bricks with their extra possessions (they were around 35% on twos and threes). Kentucky rebounded poorly but shot extremely well from the floor (57%, mostly on twos) and better from the line. Although I predicted an advantage for Kentucky shooting the ball, it wasn’t this big. They didn’t dominate the glass like I thought they would, so it’s a good thing they shot as well as they did.

In the nightcap, I had Ohio State and Kansas almost dead even. Kansas won by two points, and the game came down to the final shot. As I predicted, Kansas was better on the offensive glass. However, they turned the ball over quite a bit (I predicted the reverse), and the real difference in the game was Ohio State’s shooting. The simulations said they would shoot quite well. Instead, they shot about 35% on twos and threes, just like Louisville.  Kansas, on the other hand, shot about as expected and got the win.

I’ll have a tournament summary up tomorrow. It’s looking like a good performance for most of the prediction methods that I examined.

Third/fourth round results and final four simulations

Unfortunately, I didn’t move on to the final four of the TeamRankings blogging competition. It was fun while it lasted though, and thanks again to TeamRankings for putting it on and providing great data (which I will continue to use for NCAA tourney simulations).

It was a great third round and a so-so fourth round for my simulations. Here’s the update on the initial brackets that I’ve been tracking:

I set up the first four brackets by always choosing the “better” team according to the ranking listed on the left. The last row uses my simulations to pick the winner. I stumbled a little in round 2, but recovered strongly in the elite eight (6 out of my initial 8 predictions made it, with only Missouri and Michigan State coming up short). Pomeroy and Sagarin’s rankings proved the best at predicting the final four — both missed only Louisville (they each had Michigan State). I missed Louisville and Ohio State (I had Syracuse, by a nose).

If I forgive bracket mistakes and re-pick each game based on who actually played, here are the success rates Continue reading