◆ Orangish · Match analysis

Your match report

🔓 Full match · unlocked

Parth Khera vs Maddox Moxham · both players

Pose detection quality38/100
how confidently the players were found
Frame coverage74/100
% of frames with at least one detection
Court coverage90/100
% of detections inside the calibrated court
Player distinctness100/100
how different the two players looked
Movement plausibility92/100
% of frame-to-frame jumps within human limits
Calibration geometry100/100
how plausible the 4-corner trapezoid is
Overall 74/100. Tighter court corners and a clear, full-court camera angle raise this — and the numbers above get sharper with it.
Pose replay
SkeletonFeetWristsRacketFoot trailPlayer colours
Speed0.5×0.75×1×2×4×8×
ShowBothParth KheraMaddox Moxham
⤢ Fullscreen
Parth KheraMaddox MoxhamSwap players
Top rallies · tap to jump in
158s251s350s
Loading video…
Combined Heatmap
Player Movement
Parth Khera
0.81 miT-dom 27%
Maddox Moxham
0.80 miT-dom 35%

The ↔ arrows are each player’s last 5 lunges — the distance between their feet at full stretch. A reach of about 2 ft or more counts as a lunge; the newest is brightest and older ones fade out.

Scrub or speed it up, and tap ⤢ for fullscreen — the skeleton overlay and the heatmaps follow the playhead. Players are tracked from your tags and may occasionally swap on a crossing.

Distance covered
0.64 mi
T-dominance
28%
Top speed
8.7 mph
Total shots
796

These are best-effort metrics derived by AI from your video — they’re approximate, not exact measurements.

Coaching insights
  • Maddox Moxham held the T 56% to Parth Khera's 44% — the player who owns the centre usually dictates play, so Parth Khera should make recovering to the T after every shot the first priority.
  • Rallies ran long — a median of 7 shots with 42% past 10. The fitness is there; sharpening the finish (a tight kill or hard low drive once the opening appears) turns those grinds into points sooner.
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Calibration & player tags
Calibration frame

The teal lines show the court as it was calibrated — the boundary, service boxes and the T. If they don’t line up with the real painted court lines, the movement metrics will be off; re-calibrate with the corners on the actual floor corners and re-run.

This match analysis is free — made possible by the Friends of Squash below. To produce it, we tracked 4,852 court positions, 9,704 foot points and 82,484 body-pose joints — about 1,337 seconds of GPU compute.

This is an AI movement read — treat it as a worthwhile heuristic for shaping your training and reading competitive situations. For a fully accurate, performance-grade breakdown — ball tracking, shot categories, shot selection and patterns of movement and shots — we gladly refer you to our partner RallyVision, built for players who want to excel.

RallyVision

A heatmap shows where you moved; a coach shows you what to do with it. SQUASHTIGERSis a year-round elite junior squash academy across New Jersey, Pennsylvania and Connecticut, built for players who want to compete at the highest level. If you want coaching behind the numbers, that’s where we’d point you.

SQUASHTIGERS

Serious squash deserves a serious court. Whether it’s a new glass court or the refurbishment of an existing one — glass fronts, floors, lighting — we gladly point you to our partner STARSQUASH, the U.S. partner of CGG, which has the largest installation of all-glass squash courts worldwide. They design, build, refurbish, and can even finance courts against future bookings rather than up-front capital — the people we’d trust to do it right.

STAR SQUASH

Coach, or run a club? This free movement read is one small corner of orangish.io— the platform clubs use to run memberships, court and lesson bookings, payments, and the day-to-day of the place. If that’s your world, it’s worth seeing the whole thing.

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