Brandon Nakashima: reliable favourite conversion at 68.1%

Brandon Nakashima ranks 22 in the ATP (August 2026 snapshot) and has played 235 ATP main-draw matches in our database from 2022–2026. The headline number — 55.7% overall win rate — frames a clear mid-tier profile, but the 64.7% mark in 2026 (51 matches) shows a live uptick. The paradox worth betting on: reliable favourite conversion at 68.1%.
Key metrics at a glance
| Metric | Value |
|---|---|
| Overall win rate | 55.7% |
| ATP rank (snapshot) | 22 |
| Matches analysed | 235 (2022–2026) |
| Best surface | Grass — 60.5% |
| Weakest surface | Clay — 46.7% |
| Grand Slam win rate | 51.4% (19–18) |
| As favourite | 68.1% (n=91) |
| As underdog | 32.4% (n=71) |
Nakashima's year-by-year record
| Year | Matches | Wins | Win rate |
|---|---|---|---|
| 2022 | 58 | 35 | 60.3% |
| 2023 | 26 | 9 | 34.6% |
| 2024 | 38 | 21 | 55.3% |
| 2025 | 62 | 33 | 53.2% |
| 2026 | 51 | 33 | 64.7% |

Win rate by season, Brandon Nakashima, 2022–2026. Source: ATP match data via tennispredictor.net
Year-to-year movement shows where the market should update fastest. The favourite conversion at 68.1% and the year table are the primary form anchors for live pricing — not a single career average.
Surface breakdown: where the edge lives
| Surface | Matches | Win rate |
|---|---|---|
| Hard | 129 | 58.1% |
| Clay | 45 | 46.7% |
| Grass | 38 | 60.5% |
| Indoors | 23 | 52.2% |

Win rate by surface, Brandon Nakashima, 2022–2026. Source: ATP match data via tennispredictor.net
Surface is the first filter before round or opponent quality. Compare clay baselines in the clay-court betting guide and grass patterns in the grass-court specificity guide when translating these rates into match previews.
Tournament tier: reliability by event level
| Tournament level | Matches | Win rate |
|---|---|---|
| ATP 250 | 56 | 66.1% |
| ATP 500 | 80 | 57.5% |
| Grand Slam | 37 | 51.4% |
| Masters 1000 | 62 | 46.8% |

Win rate by tournament tier, Brandon Nakashima, 2022–2026. Source: ATP match data via tennispredictor.net
Grand Slam and Masters samples matter most for pricing; ATP 250/500 volume often inflates or deflates the headline win rate. Cross-check against Tommy Paul or Francisco Cerundolo for other ranks-21–50 archetypes.
Round-by-round: early rounds vs deep runs
| Round | Matches | Win rate |
|---|---|---|
| R1 | 85 | 65.9% |
| R2 | 38 | 63.2% |
| R3 | 19 | 36.8% |
| R16 | 50 | 46.0% |
| QF | 23 | 52.2% |
| SF | 13 | 30.8% |
| F | 4 | 50.0% |

Win rate by round, Brandon Nakashima, 2022–2026. Source: ATP match data via tennispredictor.net
Round curves separate true closers from early-round specialists. Use the drop from early rounds into deeper stages as a fade signal even when the overall win rate looks acceptable — the same structural idea as Rublev's late-round ceiling.
H2H against elite rivals
| Rival | W–L | Win rate |
|---|---|---|
| Tiafoe | 1–6 | 14.3% |
| Thompson | 4–2 | 66.7% |
| Shelton | 0–6 | 0.0% |
| Zverev | 0–5 | 0.0% |
| Bautista-Agut | 3–2 | 60.0% |
Elite H2H is sparse for many mid-ranked players; only rows with multiple meetings are shown. Use these as matchup modifiers, not as a substitute for surface form.
Favourite vs underdog split
| Role | Matches | Win rate |
|---|---|---|
| Favourite | 91 | 68.1% |
| Underdog | 71 | 32.4% |

Favourite vs underdog win rate, Brandon Nakashima, 2022–2026. Source: ATP match odds via tennispredictor.net
What the betting market misses about Brandon Nakashima
Back Brandon Nakashima when:
- on grass (60.5% over 38 matches)
- when priced as favourite (68.1% conversion, n=91)
- in first-round spots (65.9% over 85 matches)
- during strong 2026 form (64.7% over 51 matches)
Fade Brandon Nakashima when:
- on clay (46.7%)
- in R3 or later (36.8% at R3)
- against top-tier opponents with repeated negative H2H (e.g. Shelton 0–6)
For live slate context, open the predictions dashboard rather than relying on a static profile alone.
How our model treats Brandon Nakashima
The ensemble weights surface win-rate history, recent form (last 5 / last 10), and tournament-tier context most heavily for a rank-21–50 profile like this. Favourite/underdog conversion is a secondary prior when odds history is dense enough (here: 162 matches with usable prices). Uncertainty rises when the next match is on clay or in a deep-draw round where Nakashima's historical conversion collapses.
Frequently asked questions
What is Brandon Nakashima's overall win rate in this study?
55.7% across 235 ATP main-draw matches from 2022–2026.
Which surface shows the highest win rate?
Grass at 60.5% (38 matches).
How often does Brandon Nakashima win when installed as favourite?
68.1% over 91 favourite-priced matches in the cache (usable odds only).
How does Brandon Nakashima perform at Grand Slams vs regular events?
Grand Slam win rate is 51.4% (19–18), versus 55.7% overall.
Who is Brandon Nakashima's toughest matchup in the data?
Among repeated elite meetings: Shelton (0–6).
When is Brandon Nakashima worth backing or fading based on this data?
Back when on grass (60.5% over 38 matches) / when priced as favourite (68.1% conversion, n=91) / in first-round spots (65.9% over 85 matches). Fade when on clay (46.7%) / in R3 or later (36.8% at R3).
How reliable are these statistics given the sample size?
235 matches is a standard profile sample; surface and favourite splits with n≥15 are the most trustworthy rows.
How does your model handle Brandon Nakashima's surface and form swings?
It re-weights recent surface form and does not treat the career overall win rate as a constant prior when the year-by-year table shows large swings (notably the 2026 line at 64.7%).
Conclusion
Brandon Nakashima at ATP #22 is a ranks-21–50 profile defined by 55.7% overall and reliable favourite conversion at 68.1%. The actionable reads are the surface table, the favourite conversion, the 2026 form line, and the round curve — not the ranking alone.
Watch the North American hard-court swing and any surface-specific weeks to confirm whether current form holds. For related mid-tier profiles, see Frances Tiafoe and Cameron Norrie.
See today's match predictions with confidence scores and value signals.
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