Similar Players

The Similar Players tool finds players who are statistically comparable to a target player using K-Means clustering, then shows how those similar players performed against the upcoming opponent. This gives you a broader sample size to evaluate how a player archetype tends to fare in a specific matchup.

Note

The app interface is in Spanish. This guide references the actual labels you will see.

Overview

Navigate to “Herramientas” (Tools) in the sidebar and select “Jugadores Similares” (Similar Players). The route is /tools/similar-players.

The engine groups all NBA players into 18 clusters based on statistical profiles using K-Means machine learning. When you search for a player, the tool pulls others from the same cluster and shows their game logs against the upcoming opponent.

How to Use

1. Search for a player — Type at least 2 characters into the “Jugador” (Player) autocomplete search field. A dropdown of matching players appears.
2. Select a player — Click a player name from the dropdown. The tool loads similar players from the same cluster and filters their game logs against the upcoming opponent.
3. Review the results — A results card appears showing how statistically similar players performed in the same matchup.

Understanding the Results

Results Card Header

The results card displays a header reading “Jugadores similares a {playerName}” with a subheader showing the opponent and stat type being compared.

Summary Row

Below the header, a summary row provides four key metrics at a glance:

Metric	Description
Hit rate	Percentage of games where similar players went over the line. Displayed in green when 60% or above, red when 40% or below.
Hit fraction	Shows the raw count in format “{hits}/{games} OVER {referenceLine}“
Average value	Displays “avg {avgValue}” — the average stat value across all similar player games
Avg diff vs line	Shows ”+/-X.X” — how much the average value differs from the reference line

Game Log Table

The detailed table lists each individual game from similar players against the opponent:

Column	Description
Fecha	Game date in MMM d, y format
Jugador	Player name
Equipo	Team abbreviation
Valor	Actual stat value achieved (displayed in bold)
Diff	Difference vs the line — green if the player hit over, red if under

If no data is available, you will see the empty state: “Sin datos de jugadores similares disponibles para este rival.”

Tips

Tip

A high hit rate among similar players does not guarantee a hit, but it strengthens the case. Use this data alongside the engine’s score — if both agree, the pick carries more conviction.

Tip

Pay attention to the sample size shown in the hit fraction. A 75% hit rate from 4 games is far less reliable than 65% from 20 games.

The Similar Players tool complements the on-demand evaluation in the Prop Evaluator. Use the evaluator to get the engine’s full 7-block score, then cross-reference with similar player data here.

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Next: check Teammate Impact to see how lineup changes affect a player’s stat output.