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.