Win probabilities for the 2015 Gold Cup

Screen Shot 2015-07-06 at 11.37.32 AMCan the United States win the 2015 Gold Cup?

Winning the Gold Cup means more than than just bragging rights over rival Mexico. With the win, the United States qualifies for the Confederations Cup in 2017, a key tune up tournament for the World Cup in 2018.

If another team wins the Gold Cup, they play a one game playoff against the United States, winner of the 2013 Gold Cup. The winner of this playoff represents North America at the Confederations Cup.

To determine win probabilities for the Gold Cup, I combined three different estimates into an ensemble prediction. Two came from my own calculations that rank international teams on offense and defense (see the bottom of this article), and a third came from the markets.

goldcup2015

The Gold Cup usually comes down to the United States and Mexico, but Costa Rica has emerged as a solid third team that made the final 16 of the World Cup last summer.

Let’s look at these three top contenders. The offense and defense rankings come from The Power Rank algorithm and use international matches since January 1st, 2011.

United States

12th offense, 27th defense

The United States looked fantastic in beating Germany in a friendly last month, and they also beat the Netherlands in another friendly. Both matches took place on European soil.

They need to play well at the start of the Gold Cup as their group has Honduras and Panama, two top 50 teams in my world soccer/football rankings. Meanwhile, Mexico has the dregs of CONCACAF (Guatemala, Trinidad and Tobago, Cuba, all ranked lower than 80th) in their group.

Coach Jurgen Klinsmann left central defender Matt Besler, who started every game of last year’s World Cup, off the Gold Cup roster. John Brooks will most likely start, and let’s hope they can improve a defense that has ranked 27th in the world over the last 4 years.

Mexico

11th offense, 9th defense

By the numbers, Mexico has a slight edge on the United States in my rankings. They don’t have the largest win probability though since the United States will enjoy home field advantage.

I’m not 100% certain the United States should get the full .59 goals for home field. If the United States and Mexico meet in Philadelphia for the final, there will be plenty of Mexican fans wearing green in attendance.

In my ensemble calculations, the market predictions most likely account for the semi-neutral type final in Philadelphia. They gave United States a 38% win probability with Mexico at 36%. The gap was bigger in my two calculations.

For Mexico, striker Javier “Chicharito” Hernandez broke his collar bone and will miss the Gold Cup. He scored 9 goals in 33 matches for Real Madrid last season.

Costa Rica

40th offense, 10th defense

Costa Rica had an amazing World Cup last summer. In winning their group, they sent Italy and England home before the knock out stage. Then they beat Greece to advance to the Round of 16.

Costa Rica relies on its defense, which is ranked 10th in the world over the last 4 seasons. They face a road game against Canada in Toronto in the group stage, but they should win their group before most likely playing the United States in the semi-final.

List of win probabilities for the 2015 Gold Cup

1. United States, 39.1%.
2. Mexico, 33.6%.
3. Costa Rica, 11.7%.
4. Panama, 4.7%.
5. Honduras, 4.7%.
6. Jamaica, 2.3%.
7. Guatemala, 1.8%.
8. Canada, 1.4%.
9. El Salvador, 0.9%.
10. Trinidad and Tobago, 0.7%.
11. Haiti, 0.1%.
12. Cuba, 0.1%.

Preseason Big Ten college basketball rankings for 2015-16

Screen Shot 2015-07-02 at 9.28.53 AMLast month, I had the honor of being a guest on Assembly Call, the Indiana Hoosiers basketball podcast. Show runner Jerod Morris, Andy Bottoms and I discussed the upcoming college basketball season.

To prep for the show, I developed some preseason Big Ten rankings that combine calculations with subjective factors. It’s similar to what elite gamblers do in preparation for the season.

I wrote an article for Assembly Call that describes the methods and summarizes my thoughts on all 14 Big Ten teams.

My apologies in advance to Maryland fans.

And Bo Ryan’s announcement that he will retire after this season doesn’t change my mind on Wisconsin’s preseason rank, which you might find surprising.

To read the article on preseason Big Ten college basketball rankings for 2015-16, click here.

How the gambling markets view Tigers in the AL Central

Screen Shot 2015-06-30 at 3.47.14 PMIn my latest Detroit News article, I use a rich source of information to evaluate the AL Central: the gambling markets.

Each day, sports book set a moneyline for each game, and investors around the world put money on both teams. This collective wisdom of crowds drives a final price that should accurately reflect the strength of both teams.

Based on this market data, I calculate expected records for each team over the season. The results for the AL Central are surprising.

To read my article on how the markets view the AL Central, click here.

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.

Are you fooled by the randomness of baseball?

ausmus_randomnessA version of this article appeared in the Detroit News on Monday, June 15th, 2015. The ideas on how humans view randomness apply far more widely than just Tigers baseball.

Do you remember April 20th? The Tigers had an 11-2 record and looked like an offensive juggernaut.

There were many ways to rationalize the success of the Tigers. Miguel Cabrera was finally injury free and back to his first ballot Hall of Famer form. Shane Greene allowed one earned run through 3 starts and looked like a Cy Young contender.

Now let’s fast forward to June 5th. The Tigers had just dropped their 8th straight game and had a 28-28 record.

There were also many explanations for the losing streak. Ian Kinsler was cold as ice, and the Tigers couldn’t get a clutch hit if the fate of the world depended on it.

It also didn’t help that 7 of these 8 losses came against the A’s and Angels, two underrated top 10 teams in my baseball rankings.

As fans, we find all kinds of reasons for winning and losing streaks. However, there’s an additional reason for these streaks not often talked about: randomness.

Let me show you.

Random flipping of a coin

In baseball, any team can win any game, even the Phillies over the Dodgers in 2015. Because of this uncertainty, it’s useful to have a probabilistic model for game outcomes.

We’ll do a simple experiment to see the relationship between the randomness of this model and streaks. Suppose a baseball team has a 50% chance to win each game.

To see how this average team’s season plays out, I flipped a 50-50 coin on my computer 162 times. The visual shows the results through 63 games.

randomness_patterns

Team average catches fire on game 19 of the season and rips off a streak of 10 straight wins. They lose a game but then win another 4 games in a row. Two of their young pitchers look like Cy Young contenders.

However, the bottom falls out at game 34. Team average can’t buy a clutch hit as they go 6-13 over the next 19 games. Fights break out in the clubhouse because of the lack of team chemistry.

Despite the losing streak, team average still has a strong 38-25 record after 63 games.

For the record, I made zero effort to find a random sequence with streaks. I generated this sequence once for an article I wrote a few years back. Randomness looks streaky.

The next visual shows the wins and losses for the Tigers through 63 games.

tigers_2015

Both visuals contain quite a few streaks.

What the Hot Hand paper says about streaks

To understand how humans view randomness, consider a famous paper by Amos Tversky, a Stanford professor. In 1985, he published a paper called The Hot Hand in Basketball: On the Misperception of Random Sequences.

In the study, Tversky asked participants to look at a sequences of X’s and O’s that represented made and missed shots in basketball. Some of these sequences were generated at random and looked like the my coin flipping visual. However, only 32% of participants called this random shooting, while 62% called it streak shooting.

People tend to see patterns or streaks in randomness, just like you most likely saw patterns in the random sequence above.

The authors of the study also generated sequences in which it was more like to get an O after an X and a X after an O. Only with this increased tendency for an alternating sequence did more people call the sequence random shooting.

Perceptions of randomness from a young age

I’ve also done my own experiment on perceptions of randomness. At Summers-Knoll, a project based school in Ann Arbor, I brought a bag with a white and black chess piece into the kindergarten class.

I told the kids they would take turns picking a piece from the bag, which replicated the random flipping of a coin I performed on my computer above. But first, I asked them what they expected the sequence to look like.

Most of these five and six year olds wrote down an alternating sequence of X’s and O’s. A few children had a sequence of two X’s in a row. We tend to think of patterns in randomness at a very young age.

When the children picked out a chess piece from the bag, they saw that the sequence looked quite different from their expectation.

Another interesting thing happened during the experiment. After a few pulls from the bag, the children started chanting for the black piece to get picked. Cheering for randomness? That’s exactly what sports fans do. It’s ingrained in us from a very young age.

What this means for Tigers fans

From June 9th through the 14th, the Tigers played 5 games against the Cubs and Indians. They alternated wins and losses as you can see in the final 5 games of the Tigers visual above.

As humans, we view this alternating sequence random. Win some, loss some, just hope the Tigers get to the 88 wins they need to win the division. (The coin flipping experiment I did above produced 91 wins in 162 games.)

This perception changes drastically when the Tigers win 11 of 13 or drop 8 in a row. Their play no longer seems random, and fans go in search of explanations for the streak.

It’s fine to rationalize the causes behind these streaks. Baseball is far more complicated than the flipping of a 50-50 coin. However, remember that randomness alone would generate these types of streaks.