Wingfield sensors generate detailed data after every session. That data lives in XLSX exports — on the coach's laptop. Nobody else sees it.
Expensive sensors. Data gathering dust.
❓
No benchmark
Coach sees 62% first serve. But is that good for a U14 player? What's the target? There's no reference to compare against.
Numbers without context are meaningless.
🧠
Manual analysis
Coach has to interpret raw numbers themselves and decide what to train. No automatic priorities. No recommendations. Time-consuming and subjective.
Every coach interprets differently. No standard.
How It Works
Upload. Analyse. Know what to train.
01
📂
Upload XLSX
Coach uploads Wingfield XLSX export from any session or match.
02
⚙️
Auto Parse
System extracts all metrics — serve speed, first serve %, double faults, return, winners/UE ratio.
03
📊
Benchmark
Each metric compared to age-group reference values (U12 → Open). Player's position visualised.
04
🎯
Recommendations
Max 3 prioritised actions — ranked by gap from benchmark and impact. Ready for the next session.
Player Dashboard
Coach opens the app. Knows what to fix.
Novák J. · U16 · Last match Jun 10
1st Serve %
52%
Target: 60–65%
Return In %
81%
Target: 85%+
Double Faults
5
Target: max 2/match
Winners/UE Ratio
0.38
Target: >0.5
Avg Serve Speed
158 km/h
U16 benchmark: 155–170 km/h ✓
🎯 Top 3 Recommendations
1
Focus on serve consistency. First serve at 52% — below benchmark. Target: get above 60%.
2
Too many double faults. 5 in this match. Work on second serve under pressure.
3
Unforced errors too high. Ratio 0.38 — reduce unnecessary mistakes, not winners.
Recommendations prioritised by: (1) largest gap from benchmark, (2) highest impact — serve > return > rally.
Age Group Benchmarking
Know where your player stands.
Novák J. · U16 vs benchmark
MetricPlayerBenchmarkGap
1st Serve %
52%
60–65%
−8%
Double Faults
5
max 2
+3
Return In %
81%
85%+
−4%
Winners/UE
0.38
> 0.5
−0.12
Avg Serve Speed
158 km/h
155–170
✓
Age categories
U12
U14
U16
U18
Open
🎯
Context-aware
62% first serve is good for U12, below average for U18. The system always compares against the right peer group.
📈
5-session trend
Each metric shows trend over last 5 sessions. Coach sees not just current level, but direction of progress.
👤
Player's own profile
Player logs in and sees their own data. Benchmarking against peers. Motivation through numbers.
Tracked Metrics
8 metrics. Every session.
1st Serve %
60–65%
Target range
1st Serve Pts Won
70–75%
Target range
Double Faults
max 2
Per match
Return In %
85%+
Target
Return Pts Won
55%+
Target
Winners/UE Ratio
> 0.5
Target
Avg Serve Speed
by age
Per age group
0–4 Shot Pts
short
Rally analysis
All benchmarks are age-group specific — U12 through Open. Every number has context.
Progress Tracking
Season in data. Progress visible.
1st Serve % · Last 5 Sessions vs Benchmark
Player
Benchmark
Feb
Mar
Apr
May
Jun ✓
60% target
Player is below benchmark consistently. First serve is priority #1.
📚
Full history
Every uploaded session stored. Coach and player can scroll back through the entire season's data.
📉
Trend per metric
Each of the 8 metrics has its own trend chart across the last 5 sessions. See what's improving, what isn't.
🎯
Player motivation
Player sees their own data and progress. Numbers as motivation — not just the coach's word.
Recommendation Engine
Data tells you what to train next.
Rule examples
If 1st serve % < 55%
"Focus on consistency. Target: above 60%." Ranked #1 — biggest impact on the game.
If double faults ≥ 4
"Too many double faults. Work on 2nd serve under pressure."
If return in % < 75%
"Return unstable. Priority: get the ball in play."
If errors > 3× winners
"Ratio imbalanced. Reduce unforced errors — not winners."
Prioritisation logic
How the system ranks
1. Largest gap from age-group benchmark
2. Highest impact on match outcome
— Serve > Return > Rally
Maximum 3 recommendations per session. Coach gets a focused list, not a wall of feedback.
Why max 3?
Research shows coaches and players can meaningfully act on 2–3 focus areas per cycle. More recommendations = analysis paralysis. The system forces prioritisation.
Return on Investment
Your Wingfield sensors finally earning their keep.
The problem without Stats
Data ≠ Insight
Wingfield devices generate data every session. Without a platform to analyse it, benchmarks and context — that data has no value. It sits in XLSX files, unread.
What Wingfield Stats unlocks
Every session generates actionable insights, not just numbers
Coaches spend time coaching, not analysing spreadsheets
Players see their own progress — engagement increases
Federation gets standardised data across all academies
Built on proven MadStats logic — same author, same architecture, adapted for Wingfield data and tennis coaching context. Not a prototype — a battle-tested foundation.
Target Pilot
Initial rollout: ~20 coaches, ~40 players at Czech Tennis Association talent centres equipped with Wingfield sensors.
Federations with Wingfield: perfect fit. We scale from your existing hardware.
Interested?
If your federation uses Wingfield sensors, we can discuss a pilot deployment — connecting your existing data to actionable coaching insights.