Baseball cluster luck article on FiveThirtyEight

Over the past few years, I’ve been calculating cluster luck in baseball. This is based on the idea that teams can score more runs when they cluster their hits together (or allow fewer runs when pitchers scatter hits).

However, teams can’t consistently cluster hits together. Cluster luck calculations show us which teams will not keep up their torrid early season pace.

Last week, Jonah Keri used my cluster luck numbers on FiveThirtyEight to show how this has happened San Francisco and closer Sergio Romero. Then he discussed how cluster luck continues to help Seattle but regression could hit soon.

Getting him the updated cluster luck numbers was simple, as I just use widely available season totals. However, creating the above graph was a lot of work, since it required box score numbers on a daily basis.

However, the work was worth it, as I’m cooking up a way to incorporate cluster luck into my MLB rankings. It should give us a better grasp on Oakland, a team that can’t possibly be more than 1.4 runs better than MLB average.

More on this soon.

To read Jonah Keri’s article on cluster luck based on my numbers, click here.

Television Interview on Ronan Farrow Daily

me_ronan_mauriceRonan Farrow interviewed me and Maurice Edu about the World Cup on his MSNBC show yesterday.

I was pumped to meet both the host and one of the best soccer players in the United States. However, there’s not much contact when they film you from a remote location.

To do the interview, I went to a studio in my home town of Ann Arbor. A nice guy Tony set everything up.

From a remote location, I could only hear Ronan and Maurice in my earpiece. I couldn’t see them or what appeared on television.

Still, it was fun. We talked World Cup, the United States’ chances against Belgium in the Round of 16 and how numbers affect the psychology of players.

To view the interview, click here. If you’re viewing on Tuesday, July 1, it should be the main video under “Betting on the World Cup.” Otherwise, you might have to scroll through the videos on the right.

Ensemble win probabilities for the World Cup after the group stage

wc2014_winprob_ensemble_aftergroupThe wisdom of crowds.

In making predictions, it’s best to include the predictions of many different methods. Each method has its strengths and weaknesses, and taking an average gives better results.

The Power Rank began when I developed an algorithm for ranking teams. However, as I learn more about making predictions, my method will take its place along with others in an ensemble of predictors.

Ultimately, I think this will be most useful in predicting the NCAA tournament. But right now, I’m practicing on the World Cup.

My recent article on bettingexpert looks at the ensemble predictions for the World Cup after the group stage. To check it out, click here.

New international football / soccer rankings show recent form of nations

world_soccer_June19_2014The FIFA rankings suck. Not only do they poorly predict the outcome of matches, but you have to wait a month for updates.

The Power Rank international football / soccer rankings do better. The ranking algorithm considers margin of victory in adjusting for schedule strength in international soccer. As an academic study has shown, using margin of victory is critical in making predictions.

In addition, the international rankings are now updated daily.

This constant updating is interesting during the World Cup. My rankings use a 4 year window of matches and weight matches by their importance.

  • World Cup Finals: 4.
  • World Cup Qualifiers, Confederations Cup, Continental Finals: 3.
  • Continental Qualifiers, 2.
  • Friendlies, 1.

Since we’re in the middle of a World Cup, the rankings add important matches each day while dropping results from the previous World Cup. This leads to some interesting changes for certain teams.

Spain and the Netherlands

The Netherlands dominated Spain in a 5-1 win last week. This dropped an aging Spain team down to 6th. The FIFA rankings still have Spain as the top team.

The Dutch have risen to 4th. It mystifies me why more people didn’t think this traditional power could win this World Cup.

Germany and Brazil

While most other respectable rankings have Brazil on top, the weighting of matches in The Power Rank vaults Germany ahead of Brazil.

Germany has played well in the last two World Cups. In 2010, they dominated Argentina in a 4-0 rout. Just last week, they beat Portugal, another top 10 team, by the same margin.

With no weighting, Brazil would be the top team in The Power Rank.

United States and Ghana

The Yanks are 18th currently, one spot above the Ghana squad they just beat.

The United States won the game because of two great finishes by Clint Dempsey and John Brooks. However, between these two goals, Ghana dominated possession and scoring opportunities. They were the better team.

Colombia and Chile

These two South American teams are in the top 10. Colombia is ranked higher at 5th, but Chile is not far behind at 7th.

From this World Cup, the Colombia looks like the better team. They continue to score goals despite the absence of Radamel Falcao, their leading scorer in qualifying.

Moreover, my aggregated win probabilities before the World Cup gave Colombia an almost 4% chance to win it all. Chile only had a 1.9% chance.

Belgium and France

Belgium has generated much chatter as a dark horse World Cup champion. Young players like Eden Hazard have dazzled on the pitch at this World Cup.

However, their performance over the last 4 years ranks them 13th in The Power Rank. That puts them lower than France (9th), a team no has talked about as World Cup champion. (Of course, France is missing star winger Frank Ribery for this World Cup.)

Belgium’s play as a team does not make me believe they will contend for the World Cup title. My aggregated win probabilities before the tourney agree with this assessment. Belgium had the 11th highest win probability at 2.3%.

Rankings of World Cup teams

Here are rankings of the 32 World Cup teams that consider matches from June 20, 2010 through June 19, 2014. The record gives wins, losses and ties over the past 4 years. The rating gives an expected margin of victory against an average international team.

1. Germany, (37-7-11), 2.52
2. Brazil, (40-9-12), 2.28
3. Argentina, (32-8-15), 2.15
4. Netherlands, (33-9-11), 2.09
5. Colombia, (24-8-11), 2.09
6. Spain, (45-8-8), 2.05
7. Chile, (29-17-9), 1.69
8. Uruguay, (28-14-15), 1.69
9. France, (28-11-12), 1.59
10. Portugal, (26-9-13), 1.54
11. Ecuador, (17-14-15), 1.48
12. Mexico, (35-18-17), 1.48
13. Belgium, (22-8-12), 1.44
14. England, (25-8-14), 1.43
15. Ivory Coast, (31-7-9), 1.42
16. Italy, (22-12-21), 1.40
17. Ghana, (30-15-14), 1.29
18. United States, (37-17-12), 1.25
19. Russia, (24-6-13), 1.25
21. Switzerland, (20-7-12), 1.23
23. Croatia, (24-10-11), 1.16
24. Nigeria, (29-11-21), 1.11
27. Japan, (33-12-13), 1.07
28. Bosnia-Herzegovina, (21-14-7), 1.03
30. Costa Rica, (25-23-19), 0.95
32. Greece, (24-8-16), 0.91
34. Australia, (26-16-11), 0.87
35. Iran, (30-8-16), 0.85
38. South Korea, (24-17-12), 0.80
43. Honduras, (22-24-18), 0.75
50. Cameroon, (16-13-12), 0.60
53. Algeria, (19-10-6), 0.56

For all teams, click here.

Predictions

The Power Rank also provides predictions for each match and stages of the competition, both of which are update nightly.

These predictions use a different set of rankings that consider a 12 year window of games. Research as shown that these calculations are as accurate in predicting match outcomes as using a 4 year window.

Aggregating the results of many World Cup prediction models

wc2014_winprob_ensemble10Who will win the 2014 World Cup?

There is no shortage of quants making their own prediction. These range from my own at The Power Rank to the financial types at Goldman Sachs.

As much as I’d like to think my model is the best, research has shown that combining the results of many models often gives a better prediction. If you want to sound smart about combining predictions, you say the words ensemble learning.

The visual shows the average win probabilities for 10 different World Cup models, which are described at the bottom of this post. This list gives the full results.

1. Brazil, 28.64.
2. Spain, 12.62.
3. Argentina, 11.49.
4. Germany, 10.75.
5. Colombia, 3.73.
6. France, 3.72.
7. Portugal, 3.47.
8. Netherlands, 3.41.
9. Uruguay, 2.89.
10. England, 2.85.
11. Belgium, 2.34.
12. Chile, 1.87.
13. Italy, 1.84.
14. Ecuador, 1.43.
15. Russia, 1.13.
16. Ivory Coast, 1.05.
17. Mexico, 1.02.
18. United States, 0.82.
19. Switzerland, 0.78.
20. Bosnia-Herzegovina, 0.67.
21. Greece, 0.64.
22. Croatia, 0.58.
23. Japan, 0.45.
24. Nigeria, 0.44.
25. Ghana, 0.40.
26. South Korea, 0.22.
27. Iran, 0.17.
28. Algeria, 0.16.
29. Cameroon, 0.11.
30. Honduras, 0.11.
31. Costa Rica, 0.09.
32. Australia, 0.08.

The aggregated predictions neatly split up the 32 team field into 3 classes.

Brazil

First, Brazil has the highest win probability at 28.6%. This results from their status as a traditional power as well as home country advantage in this World Cup.

Research has shown that referee bias plays a large role in home advantage. Yesterday’s opening game between Brazil and Croatia was the perfect example.

Despite playing poorly overall, Brazil was awarded a penalty kick on a terrible call in the second half. Neymar converted, giving Brazil a 2-1 lead.

Then the referee missed a foul on Brazil deep in Croatia’s end of the field. As a result, Oscar scored a beautiful goal to finish off a 3-1 win.

The other three elite teams

The second class of teams consists of Spain, Argentina and Germany, teams with greater than 10% win probability each. Should Brazil stumble, one of these traditional powers should lift the trophy.

Spain won the last World Cup with their mesmerizing short passing game and stout defense. Despite the advanced age of their stars, they have a 12.6% chance of winning the World Cup.

Argentina is probably the weakest of these three teams. However, some of the models included a home continent advantage for Argentina. This puts them ahead of Germany with a 11.5% chance to win.

Germany is a dynamic young team with a potent offense. The Power Rank thinks they’re the best offensive team in the world by a significant margin. However, their defense can let them down, as it did against Italy in Euro 2012.

Most models and pundits consider Brazil, Spain, Argentina and Germany the favorites to win the World Cup.

Randomness in soccer competitions

The remaining 28 teams make up the third class of teams. There’s 36.5% chance that one of these teams wins the World Cup, the event that interests me most.

This type of “upset” has not happened recently at the World Cup. The last 5 World Cup champions are Spain, Italy, Brazil, France and (West) Germany, all traditional football powers.

However, the World Cup offers a small sample size of matches.

The group stage has three games. Have you ever looked the table of your favorite league after 3 games? The best do not necessarily rise to the top that early in the season.

After the group stage, the World Cup enters the knock out stage with 16 teams. The top teams do not always survive a single elimination tourney.

Manchester City, the Premier League champions this season, did not make the finals of the FA Cup. Hull City, 16th in the Premier League table, did.

Americans know the randomness of single elimination tourneys well from the NCAA men’s basketball tournament. This season, top teams like Arizona, Louisville and Florida failed to win the title. An unheralded Connecticut team, angry over the 1.5% win probability from The Power Rank, won the tourney.

While the World Cup hasn’t had an upset winner lately, the same is not true for the European Championships.

In 2004, Greece qualified for their first championships in 24 years. They won Euro 2004.

In 1992, Denmark only qualified when people started shooting each other in Yugoslavia. They won the competition.

Will 2014 be the year that randomness descends on the World Cup? Stay tuned.

Models used in the aggregate predictions

These 10 models were used.

  • The Power Rank, using a 12 year window of matches. A description of the simulation methodology is here, while an explanation of using such a long window of games is here.
  • The Power Rank, with a 4 year window of matches, weighted by importance of the match. These rankings were quite different from the 12 year rankings above. Germany was the top team instead of Brazil. A big reason was their 4-0 win over Argentina, a 2010 World Cup match that got 4 times the weight of a friendly in the rankings.
  • Betting markets at Bovada.
  • Infostrada, emailed to me from Simon Gleave.
  • Michael Caley at SB Nation used a least squares method that considered shots in addition to goals.
  • Numberfire.
  • Goldman Sachs. I would have never used their results had they continued to use the FIFA rankings like they did in 2010. However, they transitioned to Elo ratings, which are more accurate for international soccer.
  • David Dormagen. He aggregates a number of different rankings. Not a fan of how he calculates ties, but…
  • Roger Kaufman.
  • Bloomberg Sports, who do their own rankings.