Calculator
Compute RSI from match-by-match stats and interpret momentum.
Example data table
A short sample series for a team-performance rating (higher is better).
| Match | Rating |
|---|---|
| Match 1 | 58 |
| Match 2 | 60 |
| Match 3 | 61 |
| Match 4 | 59 |
| Match 5 | 62 |
| Match 6 | 64 |
| Match 7 | 63 |
| Match 8 | 65 |
| Match 9 | 66 |
| Match 10 | 64 |
| Match 11 | 67 |
| Match 12 | 69 |
| Match 13 | 68 |
| Match 14 | 70 |
| Match 15 | 72 |
| Match 16 | 71 |
| Match 17 | 73 |
| Match 18 | 74 |
Formula used
- Change = Current − Previous (or inverted when lower is better).
- Gain = max(Change, 0), Loss = max(−Change, 0).
- Initial averages (first N changes): AvgGain = ΣGain / N, AvgLoss = ΣLoss / N.
- Wilder smoothing (next points): AvgGain = (PrevAvgGain·(N−1)+Gain)/N and same for loss.
- RS = AvgGain / AvgLoss, then RSI = 100 − 100/(1+RS).
- Edge cases: if AvgLoss is zero, RSI becomes 100; if both averages are zero, RSI is 50.
How to use this calculator
- Pick a sport and name your metric (points, rating, pace, time).
- Enter match-by-match values in order, oldest to newest.
- Set N, plus overbought/oversold thresholds for your context.
- Enable “Lower is better” for timing metrics (seconds, lap splits).
- Click Calculate to see the latest RSI and the full table.
- Export CSV for analysis, or PDF for sharing with staff.
Notes for sports interpretation
- RSI above your upper threshold can indicate short-term strain or peak form.
- RSI below your lower threshold can highlight dips, fatigue, or adaptation phases.
- Trend matters: rising RSI often signals improving momentum; falling RSI can warn of regression.
Momentum quantification across fixtures
Relative strength index converts sequential match metrics into a 0–100 momentum score. Using a 14‑game window, the first RSI prints after 15 data points. Readings above 70 often coincide with bursts of dominance, and readings below 30 reflect extended slumps. Coaches can monitor weekly changes to detect turning points earlier than raw averages. Treat RSI as a trend lens, not a prediction.
Selecting the right performance metric
RSI works on any ordered series: points scored, expected goals, player rating, serve speed, or pace. Prefer stable metrics with consistent scale, such as points differential per game or rolling efficiency. Record values at a fixed cadence and keep them chronological. For “lower is better” measures like sprint seconds, invert the direction so improvements count as gains. Keep one metric per run to avoid mixing noise from different units.
Tuning period and thresholds
Short periods (7–10) react quickly but amplify variance after exceptional games. Longer periods (20–30) smooth randomness and highlight sustained form. Thresholds should fit your sport’s volatility; 65/35 may suit tight leagues, while 75/25 can suit high‑scoring formats. Recalibrate using prior seasons: pick thresholds that flag only the top and bottom 10–20% of RSI observations. Use the same N when comparing athletes to keep signals comparable.
Integrating workload and context
Pair RSI with training load, minutes played, and opponent strength. A rising RSI with increasing minutes can indicate overextension risk, especially if recovery markers trend down. A low RSI after travel congestion may be fatigue rather than tactical failure. Segment by context when needed: home versus away, surface type, or altitude. Use context tags in labels—home/away, surface, weather—to explain outliers and avoid overreacting to single games.
Reporting and review workflow
Use the results table to audit every step: change, gain, loss, and Wilder‑smoothed averages. Export CSV for dashboards and correlation checks against injuries or win probability. Share the PDF with staff as a match briefing attachment. Set simple alerts, such as an RSI move of 8+ points in two fixtures, or a cross of your thresholds. In reviews, focus on RSI direction and divergence from scorelines, then adjust tactics, rotations, or training intensity accordingly.
FAQs
1) What sports stats work best for RSI?
Use a single metric with a consistent scale, like points differential, efficiency rating, expected goals, or player rating. Avoid mixing units. Stable metrics produce cleaner momentum signals.
2) How many values do I need before RSI appears?
You need at least N+1 values. A 14‑period RSI requires 15 ordered entries, because RSI is based on N changes between consecutive values.
3) When should I enable “Lower is better”?
Enable it for timing and error metrics where decreases indicate improvement, such as sprint seconds, lap splits, reaction time, or penalty counts. The calculator flips the sign so improvements behave like gains.
4) Are 70/30 thresholds always correct?
No. They are common defaults, but sport volatility differs. Try 65/35 for steadier leagues or 75/25 for high‑variance formats, then validate against historical ranges for your team.
5) Can I compare athletes with different match counts?
Yes, if both have at least N+1 entries and you use the same period and thresholds. Interpret with care when schedules differ, because opponent strength and role changes can shift the series.
6) How should I use RSI in coaching decisions?
Use RSI to support reviews, not replace them. Combine it with workload, injuries, tactical notes, and video. Watch for RSI trend changes and threshold crossings to time rotations or training adjustments.