League EuroLeague
Rock & Correlation ยท Model Lab

2-Point or 3-Point Shooting: What Matters More for Winning?

A hot night from three can change a game in minutes. But is three-point accuracy actually more closely connected to winning than two-point accuracy? We analyzed 3,648 EuroLeague games from 2014 through 2025 to find out.

The short answer: both matter, but three-point shooting percentage showed the stronger relationship with winning. The best signal, however, came from using both together.

What We Compared

For every game, we calculated the difference between the two teams in 2-point shooting percentage and in 3-point shooting percentage. We then examined how strongly each shooting advantage was associated with the game result.

This is an association analysis, not a claim that shooting percentage alone causes a win. Basketball outcomes depend on many factors, including possessions, turnovers, rebounds, free throws and opponent quality.

The First Result: Winners Shoot Better

MetricWinnersLosersAverage gap
2PT%56.4%51.2%+5.2 pp
3PT%39.8%33.0%+6.7 pp

Winning teams were better from both ranges. But the average winner-loser gap was larger from three: about 6.7 percentage points, compared with 5.2 percentage points from two.

How Often Did the Better-Shooting Team Win?

68.0%of games: the winner had the better 2PT%
69.7%of games: the winner had the better 3PT%

The difference is not enormous, but it points in the same direction: outperforming the opponent from three was slightly more common among winners.

Correlation With Winning

Next, we measured the relationship between the shooting-percentage advantage and winning. The 3-point percentage differential produced the stronger correlation.

2PT% gap
0.415
3PT% gap
0.473

A larger positive difference means a team shot better than its opponent. For both shot types that advantage was clearly associated with winning, but the relationship was stronger for 3PT%.

A Prediction Test Tells the Same Story

We also ran simple logistic models using the shooting-percentage differences to distinguish winners from losers. We evaluated them with AUC, where 0.50 represents no discrimination and higher values indicate a stronger ability to separate wins from losses.

Information usedAUC
2PT% difference only0.748
3PT% difference only0.783
2PT% + 3PT% differences0.885

The three-point differential was more informative on its own. But the strongest result came when both shooting measures were combined. Two-point and three-point efficiency contain different information, and the model benefits from having both.

Is This Just a Recent Three-Point Trend?

Not entirely. Looking season by season, the 3PT% differential had a stronger correlation with winning than the 2PT% differential in 10 of the 12 seasons in the dataset. The exceptions were 2014 and 2020.

There are also signs that the gap has been particularly noticeable in several recent seasons. For example, in 2023 and 2024 the correlation for the 3PT% differential was approximately 0.481 and 0.494, while the corresponding 2PT% correlations were about 0.328 and 0.315.

Season-by-season correlations can move around because of sample size, league composition and style of play, so they should be interpreted as descriptive evidence rather than a stable law of basketball.

Why Might Three-Point Accuracy Matter More?

The obvious reason is the value of the shot. A made three is worth 50% more than a made two. When a team combines high three-point volume with an unusually efficient shooting night, the scoreboard impact can become large very quickly.

But there is another modeling lesson here: the most important variable is not necessarily the one we should use alone. Three-point percentage had the stronger individual relationship with winning, yet combining 2PT% and 3PT% produced substantially better separation than either variable by itself.

So, Two or Three?

If we have to choose one based on this dataset, the answer is three. The 3PT% advantage had a stronger correlation with winning, winners had a larger average advantage from three, and a model based only on 3PT% differential outperformed one based only on 2PT% differential. But for prediction, the better answer is: use both.

What This Means for DunkOnOdds

This is exactly why our prediction process looks beyond the final score or league standings. Shooting efficiency contains useful information, but basketball games are multi-factor systems. A useful model needs to combine signals rather than search for one magic statistic.

Data: DunkOnOdds EuroLeague database, seasons 2014-2025. Analysis includes 3,648 games with complete shooting and result data. Percentage-point figures may differ slightly due to rounding.