Player comparison engine
Side-by-side breakdowns across 40+ performance metrics. Adjust the season range, pick a position group, and see who actually improved — not just who scored more.
Cut evaluation time from hours to minutesStatPulse turns raw game data into clear player profiles, historical trends and scouting-grade metrics. Built for analysts who want context, not just numbers.
Side-by-side breakdowns across 40+ performance metrics. Adjust the season range, pick a position group, and see who actually improved — not just who scored more.
Cut evaluation time from hours to minutesTwenty years of game logs, indexed by season, opponent, and venue. Trace how a player's output shifts after a coaching change or a rule adjustment.
Spot decline curves before they hit the box scoreIsolate performance by home and away splits, rest days, or strength of schedule. The same player looks different against a top-five defense — now you can prove it.
Understand the situation behind every stat lineGenerate a clean PDF or CSV summary for any player, with your own notes attached. Built for the analyst who has to present findings to a front office.
Turn raw data into a shareable documentFollow key metrics as they update during a match. See momentum swings in real time and flag players who are trending above their season average.
React to the game while it's still happeningChoose the data layer that fits how deep you want to go. Every tier includes the full historical archive; the difference is in the tools and export options.
Season-by-season leaderboards, basic player cards, and standard performance charts. Built for casual readers who want clean numbers without the noise.
02Adds advanced metrics like usage rate, defensive win shares, and rolling form curves. Includes CSV export for the current season and custom date-range queries.
03Full API access, multi-season comparisons, and percentile rankings across the entire athlete database. Designed for analysts who build their own models and reports.
04Shared workspaces, scheduled data pulls, and priority support. For scouting departments and research groups that need consistent, auditable data pipelines.
We built StatPulse for people who need more than a box score. The platform combines historical records, player tracking, and advanced metrics into one workspace — so you can evaluate performance with context, not guesswork.
See how a player’s efficiency changes against top defenses, in clutch minutes, or after a position change. Our metrics adjust for opponent strength and game situation, so you’re not comparing raw numbers in a vacuum.
Dig into 20+ seasons of player and team data. Build custom baselines, track development curves, and spot breakout candidates before the mainstream stats catch up.
Turn raw tracking data into clear scouting summaries. Filter by speed, acceleration, workload, or defensive impact — then export a clean report for your draft board or trade analysis.
Drag, filter, and compare metrics directly on the dashboard. Save your custom views and share them with your team. No SQL, no export-import dance.
Whether you’re covering a league, coaching a club, or writing a deep-dive piece, StatPulse gives you the numbers and the context to back up your take.
Three long-form pieces that go deeper into the metrics, tools, and historical patterns we use every day in player evaluation.
PER is a staple of modern analytics, but it has blind spots. This article breaks down how the rating is calculated, where it falls short, and why combining it with defensive metrics gives a fuller picture. We also look at how teams use these numbers in real scouting decisions, from draft picks to trade negotiations.
GPS vests and heart-rate monitors now track every sprint, jump, and change of direction. This post explores the technology behind them, the data they generate, and how coaches turn that into actionable plans. We also discuss privacy concerns and the risk of over-reliance on numbers without context.
Longitudinal data reveals patterns that single-season samples miss. This article examines how historical statistics help analysts identify breakout candidates, spot decline curves, and understand the impact of rule changes. We highlight case studies from basketball and soccer, showing where models succeed and where they fail.