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How FiveThirtyEight Is Forecasting The 2017 NCAA Tournament

Editor’s note: This article is an adapted version of one we published last year about how our March Madness predictions work.

Welcome to FiveThirtyEight’s March Madness predictions of the men’s and women’s NCAA basketball tournaments. We’ve been issuing probabilistic March Madness forecasts in some form since 2011, when FiveThirtyEight was just a couple of us writing for The New York Times.

Here’s how we computed everything in this year’s forecast.

Live win probabilities

Our interactive graphic will include a dashboard that shows the score and time remaining in every game as it’s played, as well as the chance that each team will win that game. These probabilities are derived using logistic regression analysis, which lets us plug the current state of a game into a model to produce the probability that either team wins the game. Specifically, we used play-by-play data from the past five seasons of Division I NCAA basketball to fit a model that incorporates:

  • Time remaining in the game
  • Score difference
  • Pre-game win probabilities
  • Which team has possession, with a special adjustment if the team is shooting free throws.

These in-game win probabilities won’t account for everything. If a key player has fouled out of a game, for example, his or her team’s win probability is probably a bit lower than we’ve listed. There are also a few places where the model experiences momentary uncertainty: In the handful of seconds between the moment when a player is fouled and the free throws that follow, we use the team’s average free-throw percentage. Still, these probabilities ought to do a reasonably good job of showing which games are competitive and which are in the bag.

We built a separate in-game probability model for the women’s tournament that works in exactly the same way but uses historical women’s data. Thus, we’ll be updating our forecasts live for both the men’s and women’s tournament.

Excitement index

Our March Madness “excitement index” (loosely based on Brian Burke’s NFL work) is a measure of how much each team’s chances of winning changed over the course of the game and is a good reference for picking the best games to flip to.

The calculation is simple: It’s the average change in win probability per basket scored, weighted by the amount of time remaining in the game. This means that a late-game basket has more influence on a game’s rating than a basket near the beginning of the game. We give additional weight to changes in win probability in overtime. Ratings range from 0 to 10, except in extreme cases where they can exceed 10.

Elo ratings

Otherwise, the methodology for our men’s forecasts is also largely the same as last year. But we’ve developed our own computer rating system — Elo — which we include along with the five computer rankings and two human rankings we used previously.

If you’ve followed FiveThirtyEight, you’ll know that we’re big fans of Elo ratings, which we’ve introduced for the NBA, the NFL and other sports. We’ve now applied them for men’s college basketball teams dating back to the 1950s, using game data from ESPN, Sports-Reference.com and other sources.

Our methodology for calculating these Elo ratings is highly similar to the one we use for NBA. They rely on relatively simple information — specifically, the final score, home-court advantage, and the location of each game. (College basketball teams perform significantly worse when they travel a long distance to play a game.) They also account for a team’s conference — at the beginning of each season, a team’s Elo rating is regressed toward the mean of other schools in its conference — and whether the game was an NCAA Tournament game. We’ve found that historically, there are actually fewer upsets in the NCAA Tournament than you’d expect from the difference in teams’ Elo ratings, perhaps because the games are played under better and fairer conditions in the tournament than in the regular season. Our Elo ratings account for this and also weight tournament games slightly higher than regular season ones.

Elo ratings for the 68 teams to qualify for the men’s tournament follow below.

RATINGS PROBABILITY OF…
TEAM REGION SEED ELO COMPOSITE FINAL 4 CHAMPS
Villanova East 1 2142 95.2 40.2% 15.0%
Gonzaga West 1 2029 93.7 41.5 13.8
Kansas Midwest 1 2058 92.2 38.0 10.4
Kentucky South 2 2054 92.3 30.2 8.2
North Carolina South 1 2030 91.7 29.9 7.0
Duke East 2 2044 92.3 23.7 6.7
Louisville Midwest 2 1978 90.8 21.6 5.0
Arizona West 2 2038 89.0 16.1 4.4
West Virginia West 4 1966 90.8 14.7 3.5
UCLA South 3 1965 88.0 9.8 2.5
Virginia East 5 1924 90.0 9.6 2.5
Saint Mary’s (CA) West 7 1888 87.4 11.8 2.1
Purdue Midwest 4 1932 88.6 10.6 2.0
Wichita State South 10 1972 88.9 8.4 2.0
Southern Methodist East 6 2019 88.4 7.2 1.7
Iowa State Midwest 5 1959 87.9 9.0 1.7
Baylor East 3 1925 87.7 6.4 1.4
Oregon Midwest 3 2026 87.3 6.6 1.2
Butler South 4 1892 86.5 8.6 1.1
Florida East 4 1946 87.8 5.7 1.1
Florida State West 3 1897 87.2 7.0 1.0
Cincinnati South 6 1903 87.4 5.3 0.9
Wisconsin East 8 1874 87.8 4.4 0.9
Michigan Midwest 7 1968 86.9 5.0 0.8
Notre Dame West 5 1932 86.7 3.9 0.6
Creighton Midwest 6 1887 84.4 2.8 0.4
Oklahoma State Midwest 10 1863 84.7 2.0 0.3
Miami (FL) Midwest 8 1867 84.6 1.6 0.2
Arkansas South 8 1827 83.2 1.7 0.2
Vanderbilt West 9 1816 83.8 1.3 0.1
Rhode Island Midwest 11 1847 84.0 1.3 0.1
Kansas State South 11 1745 83.1 0.8 0.1
South Carolina East 7 1745 83.1 1.1 0.1
Seton Hall South 9 1864 83.0 1.2 0.1
Dayton South 7 1800 82.8 1.1 0.1
Marquette East 10 1830 83.0 0.9 0.1
Michigan State Midwest 9 1791 82.8 1.0 <0.1
Wake Forest South 11 1797 83.0 0.7 <0.1
Xavier West 11 1773 82.3 0.9 <0.1
Virginia Commonwealth West 10 1823 82.9 0.9 <0.1
Middle Tennessee South 12 1816 81.3 1.2 <0.1
Maryland West 6 1754 82.5 0.9 <0.1
Northwestern West 8 1764 82.6 0.8 <0.1
Minnesota South 5 1827 81.2 1.0 <0.1
Providence East 11 1805 81.8 0.3 <0.1
Southern California East 11 1764 81.2 0.2 <0.1
Nevada Midwest 12 1827 80.7 0.2 <0.1
Princeton West 12 1824 80.0 0.2 <0.1
North Carolina-Wilmington East 12 1798 80.2 0.2 <0.1
Virginia Tech East 9 1822 80.0 0.1 <0.1
Vermont Midwest 13 1786 79.5 0.1 <0.1
Bucknell West 13 1679 77.9 0.1 <0.1
East Tennessee State East 13 1721 78.1 0.1 <0.1
Winthrop South 13 1664 75.5 0.1 <0.1
Florida Gulf Coast West 14 1619 75.8 <0.1 <0.1
New Mexico State East 14 1630 75.6 <0.1 <0.1
Iona Midwest 14 1608 75.5 <0.1 <0.1
Kent State South 14 1625 74.3 <0.1 <0.1
Troy East 15 1643 73.3 <0.1 <0.1
Northern Kentucky South 15 1614 72.8 <0.1 <0.1
South Dakota State West 16 1624 72.8 <0.1 <0.1
North Dakota West 15 1591 72.3 <0.1 <0.1
Texas Southern South 16 1502 71.0 <0.1 <0.1
Jacksonville State Midwest 15 1548 71.2 <0.1 <0.1
North Carolina Central Midwest 16 1513 71.0 <0.1 <0.1
UC-Davis Midwest 16 1528 69.9 <0.1 <0.1
Mount St. Mary’s East 16 1454 69.8 <0.1 <0.1
New Orleans East 16 1524 69.2 <0.1 <0.1
2017 NCAA Tournament team ratings

Note, however, that Elo is still just one of six computer rankings that we use for the men’s tournament. The other five are ESPN’s BPI, Jeff Sagarin’s “predictor” ratings, Ken Pomeroy’s ratings, Joel Sokol’s LRMC ratings, and Sonny Moore’s computer power ratings. In addition, we use two human-generated rating systems: the selection committee’s 68-team “S-Curve”, and a composite of preseason ratings from coaches and media polls. The eight systems — six computer-generated and two human-generated — are weighted equally in coming up with a team’s overall rating.

We’ve calculated Elo ratings for men’s teams only. For women’s ratings, we rely on the same composite of ratings systems that we used last year. You can find more about the methodology for our women’s forecasts here.

As has been the case previously, our ratings are also adjusted for travel distance and (for men’s teams only) player injuries. Our injury adjustment has been slightly improved to account for the higher or lower caliber of replacement players on different teams.

Jay Boice is a computational journalist for FiveThirtyEight.

Nate Silver is the founder and editor in chief of FiveThirtyEight.

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