World Cup team total goals by analytics

France brings a formidable squad to the 2018 World Cup. This talent rich team has a 10.1% chance to win according to my ensemble numbers, 4th best among all teams.

The markets also like France. They not only have one of the best chances to win the tourney, but the markets say this France team will score 9.5 goals.

However, the numbers suggest this goal total is high. To analyze the tournament, I simulate it 50,000 times based on offense and defense rankings that take match results and adjust for strength of schedule. These simulations allow me calculate the win probability for each team.

Recently, I adapted my code so that it also calculates the number of goals each team scores. Note this team goal total depends on how far the team is likely to advance. Favorites like Brazil and France have the highest goal totals, while long shots like Saudi Arabia and Iran have the lowest.

The simulations give a goal total of 7.4 for France, much less than the market value of 9.5 goals.

We can dig further into the analytics to uncover why France isn’t expected to score up to their talent level. They tend to cross the ball, which leads to contested headers with a low probability of success. This is fine for less talented teams like the United States, but it’s a waste when you have strikers like Antoine Griezmann and Kylian Mbappe.

According to a recent article on FiveThirtyEight, crosses make up 23.5% of France’s successful passes into the opponent’s penalty area. This rate is the 15th highest among 32 World Cup teams and a travesty for a team with loads of attacking talent.

The Power Rank 2018 World Cup Analytics now has projected total team goals for all 32 teams. It also has the following:

  • Ensemble prediction for the win probability for each team
  • Ensemble probability for each team to advance to the knockout stage
  • Win, loss, draw probability for each group stage match
  • Notes on injuries and situations for each team

To get The Power Rank 2018 World Cup Analytics, click on “I want this!”

Podcast: Mike Goodman on World Cup predictions, soccer analytics

On this episode of The Football Analytics Show, I talk with Mike Goodman, a writer who explains the finer points of soccer analytics. His content has appeared on FiveThirtyEight, The Athletic and Grantland among other outlets, and he’s the managing editor at Stats Bomb.

In previewing the World Cup, we discuss:

  • The evolution of soccer analytics from goals to shots to expected goals
  • The reason goal based analytics is still useful
  • Mike’s top 4 countries that could win the 2018 World Cup
  • Why the numbers underestimate Spain
  • How expected goals might criticize the roster construction of Spain, Belgium

Mike does such an excellent job explaining the analytics, and I highly recommend listening to get up to speed on analytics before the World Cup.

Historian Ron Chernow dominates the non-soccer part of the conversation that ends the show.

To listen to the show in iTunes, click here.

To listen to the show here, click on the right pointing triangle.

Win probability for the 2018 NBA Finals

For the 2018 playoffs, I’ve been using data from game results and markets and then filtering to attempt to account for injuries. More details below.

For the 2018 NBA Finals, my model says that Golden State is better than Cleveland by 6.2 points on a neutral court. This leads to the following odds for the series.

Golden State has a 89.6 percent chance of winning the series.

As of Tuesday afternoon on May 29th, 2018, Bookmaker had Golden State -1100 to win the series (Cleveland is +750). This implies a 88.6% chance to win the series for Golden State once you account for the vig.

I’m really surprised the numbers match up so well. The NBA playoffs have been frustrating to predict from a numbers perspective because of injuries and Golden State’s underachieving. Here’s how my model works.

First, I took the game results from the season and kept only games in which teams had all their key players. For example, Golden State had 38 games in which Steph Curry, Klay Thompson, Kevin Durant and Draymond Green all played.

This reduces the set of games, but it gives a better picture of how a team might perform with its top players. It also assumes Cleveland’s Kevin Love will play. With this reduced set of games, I take margin of victory and adjust for schedule with my ranking algorithm.

Second, I take the closing point spreads in the markets since January 1st and perform the same filtering process with top players. I generate market rankings by adjusting these spreads for schedule.

Then I blended these two rankings to give the following rankings of playoff teams. The rating gives an expected margin of victory against an average NBA team on a neutral court.

1. Golden State, 8.89
2. Houston, 8.11
3. Toronto, 4.59
4. Cleveland, 2.72
5. Philadelphia, 2.32
6. Oklahoma City, 2.17
7. Utah, 2.10
8. Boston, 1.44
9. San Antonio, 1.35
10. Minnesota, 1.26
11. Washington, 1.26
12. Portland, 0.79
13. Indiana, 0.70
14. New Orleans, 0.69
15. Milwaukee, 0.28
16. Denver, 0.26
17. Los Angeles Clippers, -0.60
18. Miami, -1.23
19. Charlotte, -1.39
20. Detroit, -1.46
21. Dallas, -2.71
22. Los Angeles Lakers, -3.68
23. Memphis, -4.27
24. New York, -4.51
25. Orlando, -4.73
26. Brooklyn, -5.14
27. Chicago, -5.44
28. Atlanta, -5.50
29. Phoenix, -6.67
30. Sacramento, -7.35

Podcast: David Purdum on legalizing sports betting

On this episode of The Football Analytics Show, I welcome David Purdum, writer for ESPN Chalk. We discuss the recent Supreme Court decision that opens the door for states to allow sports betting.

Among other topics, we discuss:

  • The benefits of a federal versus state by state framework
  • The reason why offshore sports book are still illegal
  • How the new legal framework puts the NCAA in a difficult situation
  • The role that tech giants like Amazon and Google might play

As much as I enjoyed talking to David about sports, I had an even better time talking about Hunter S. Thompson and Jack White later in the show.

To listen on iTunes, click here.

To listen here, click on the right pointing triangle.

Check out the 2018 baseball predictions

Every morning, I post predictions for Major League Baseball games based on my team rankings and starting pitchers.

The team rankings consist of two parts:

  • preseason expectations based on win totals
  • expected runs adjusted for strength of schedule

In early May, the weight of the second factor that uses data from the current season overtakes the preseason numbers. This weight increases with each passing day of the season. For starting pitchers, I use the Zips predictions on Fangraphs.

May has usually been a good month for these predictions when look at the win rate of the team with the higher win probability. Let’s compare this with the team favored by the moneyline in the markets.

  • 2016: The Power Rank, 56.8%. Markets, 60.7%.
  • 2017: The Power Rank, 56.7%, Markets 56.2%.

The predictions have also been good early this season. From April 9, 2018 through May 8, 2018, The Power Rank has predicted 58.9% of game winners (231-185) compared to 57.8% for the median closing moneyline in the markets. I don’t expect this type of accuracy compared to the markets later in the season.

My numbers haven’t given up on teams like the Los Angeles Dodgers. Despite a 15-20 record early this season, the Dodgers are still ranked 9th when I take expected runs and adjust for strength of schedule.

The numbers do not account for injuries, so please make the necessary adjustments.

To check out the daily baseball predictions, click on the primary predictions page.