Stat Leaders
See who leads every division. Rank UFC fighters by CCR and TSR, striking, grappling, and control—with every metric measured against their weight class peers.
Patricio Pitbull
Featherweight
106.1
CCR
Robert Valentin
Middleweight
106.1
CCR
Drew Dober
Lightweight
106.0
CCR
Michal Oleksiejczuk
Middleweight
106.0
CCR
Stephen Thompson
Welterweight
106.0
CCR
Jailton Almeida
Heavyweight
105.9
CCR
Sebastian Szalay
Featherweight
105.9
CCR
Theodor Berggren
Welterweight
105.9
CCR
Chris Curtis
Welterweight
105.9
CCR
Christian Edwards
Catch Weight
105.9
CCR
Marcin Tybura
Heavyweight
105.8
CCR
Guram Kutateladze
Lightweight
105.7
CCR
Shi Ming
Women's Strawweight
105.7
CCR
Alexis Davis
Women's Bantamweight
105.7
CCR
Jordan Vucenic
Lightweight
105.6
CCR
Feng Xiaocan
Women's Strawweight
105.6
CCR
Jeremiah Wells
Welterweight
105.6
CCR
Melissa Gatto
Women's Flyweight
105.6
CCR
Baergeng Jieleyisi
Bantamweight
105.5
CCR
Jairzinho Rozenstruik
Heavyweight
105.4
CCR
Michael Johnson
Lightweight
105.4
CCR
Serghei Spivac
Heavyweight
105.3
CCR
Alex Caceres
Featherweight
105.2
CCR
Oban Elliott
Welterweight
105.2
CCR
Danny Barlow
Middleweight
105.2
CCR
Lucas Rocha
Flyweight
105.1
CCR
Miles Johns
Bantamweight
105.1
CCR
Marc Diakiese
Lightweight
105.0
CCR
Loik Radzhabov
Lightweight
105.0
CCR
Tre'ston Vines
Middleweight
104.9
CCR
Robert Bryczek
Middleweight
104.9
CCR
Charles Radtke
Welterweight
104.9
CCR
Abdul-Kareem Al-Selwady
Lightweight
104.9
CCR
Marlon Vera
Bantamweight
104.9
CCR
Jefferson Nascimento
Welterweight
104.8
CCR
Jonny Parsons
Welterweight
104.8
CCR
Bassil Hafez
Welterweight
104.8
CCR
Chidi Njokuani
Welterweight
104.7
CCR
Said Nurmagomedov
Bantamweight
104.6
CCR
Luana Carolina
Women's Bantamweight
104.6
CCR
Jake Hadley
Bantamweight
104.6
CCR
Marvin Vettori
Middleweight
104.5
CCR
Ollie Schmid
Featherweight
104.5
CCR
Yi Sak Lee
Middleweight
104.3
CCR
Chase Hooper
Lightweight
104.3
CCR
Jordan Leavitt
Featherweight
104.1
CCR
Luis Saldana
Featherweight
104.1
CCR
Julia Polastri
Women's Strawweight
104.1
CCR
Melissa Mullins
Women's Bantamweight
104.0
CCR
Bekzat Almakhan
Bantamweight
103.9
CCR
| Rank | Fighter | Weight class | CCR | Percentile | Percentile per weight class | SLpM | TD / min |
|---|---|---|---|---|---|---|---|
| 451 | Patricio Pitbull | Featherweight | 106.1 | 59.6% | 59.4% | 2.7 | 0.1 |
| 452 | Robert Valentin | Middleweight | 106.1 | 59.6% | 60.0% | 1.3 | 0.0 |
| 453 | Drew Dober | Lightweight | 106.0 | 59.5% | 63.4% | 4.7 | 0.0 |
| 454 | Michal Oleksiejczuk | Middleweight | 106.0 | 59.4% | 59.1% | 6.4 | 0.1 |
| 455 | Stephen Thompson | Welterweight | 106.0 | 59.3% | 61.2% | 4.0 | 0.0 |
| 456 | Jailton Almeida | Heavyweight | 105.9 | 59.2% | 55.3% | 2.4 | 0.4 |
| 457 | Sebastian Szalay | Featherweight | 105.9 | 59.1% | 58.7% | 5.7 | 0.1 |
| 458 | Theodor Berggren | Welterweight | 105.9 | 59.0% | 60.4% | 3.5 | 0.0 |
| 459 | Chris Curtis | Welterweight | 105.9 | 58.9% | 59.7% | 5.4 | 0.0 |
| 460 | Christian Edwards | Catch Weight | 105.9 | 58.8% | 57.1% | 2.4 | 0.0 |
| 461 | Marcin Tybura | Heavyweight | 105.8 | 58.7% | 53.9% | 3.4 | 0.0 |
| 462 | Guram Kutateladze | Lightweight | 105.7 | 58.7% | 62.8% | 3.8 | 0.1 |
| 463 | Shi Ming | Women's Strawweight | 105.7 | 58.6% | 61.6% | 2.4 | 0.1 |
| 464 | Alexis Davis | Women's Bantamweight | 105.7 | 58.5% | 57.1% | 5.7 | 0.1 |
| 465 | Jordan Vucenic | Lightweight | 105.6 | 58.4% | 62.1% | 2.1 | 0.0 |
| 466 | Feng Xiaocan | Women's Strawweight | 105.6 | 58.3% | 60.3% | 4.5 | 0.0 |
| 467 | Jeremiah Wells | Welterweight | 105.6 | 58.2% | 59.0% | 2.2 | 0.2 |
| 468 | Melissa Gatto | Women's Flyweight | 105.6 | 58.1% | 61.4% | 3.5 | 0.1 |
| 469 | Baergeng Jieleyisi | Bantamweight | 105.5 | 58.0% | 59.3% | 0.6 | 0.1 |
| 470 | Jairzinho Rozenstruik | Heavyweight | 105.4 | 57.9% | 52.6% | 3.8 | 0.0 |
| 471 | Michael Johnson | Lightweight | 105.4 | 57.8% | 61.4% | 4.5 | 0.1 |
| 472 | Serghei Spivac | Heavyweight | 105.3 | 57.8% | 51.3% | 3.3 | 0.3 |
| 473 | Alex Caceres | Featherweight | 105.2 | 57.7% | 58.0% | 4.6 | 0.0 |
| 474 | Oban Elliott | Welterweight | 105.2 | 57.6% | 58.3% | 3.6 | 0.1 |
| 475 | Danny Barlow | Middleweight | 105.2 | 57.5% | 58.3% | 5.6 | 0.0 |
| 476 | Lucas Rocha | Flyweight | 105.1 | 57.4% | 51.4% | 5.9 | 0.1 |
| 477 | Miles Johns | Bantamweight | 105.1 | 57.3% | 58.7% | 3.4 | 0.1 |
| 478 | Marc Diakiese | Lightweight | 105.0 | 57.2% | 60.7% | 1.7 | 0.3 |
| 479 | Loik Radzhabov | Lightweight | 105.0 | 57.1% | 60.0% | 2.9 | 0.3 |
| 480 | Tre'ston Vines | Middleweight | 104.9 | 57.0% | 57.4% | 0.0 | 0.0 |
| 481 | Robert Bryczek | Middleweight | 104.9 | 57.0% | 56.5% | 2.8 | 0.0 |
| 482 | Charles Radtke | Welterweight | 104.9 | 56.9% | 57.6% | 2.5 | 0.1 |
| 483 | Abdul-Kareem Al-Selwady | Lightweight | 104.9 | 56.8% | 59.3% | 4.6 | 0.1 |
| 484 | Marlon Vera | Bantamweight | 104.9 | 56.7% | 58.0% | 4.5 | 0.0 |
| 485 | Jefferson Nascimento | Welterweight | 104.8 | 56.6% | 56.8% | 2.4 | 0.0 |
| 486 | Jonny Parsons | Welterweight | 104.8 | 56.5% | 56.1% | 6.3 | 0.0 |
| 487 | Bassil Hafez | Welterweight | 104.8 | 56.4% | 55.4% | 3.7 | 0.1 |
| 488 | Chidi Njokuani | Welterweight | 104.7 | 56.3% | 54.7% | 5.7 | 0.0 |
| 489 | Said Nurmagomedov | Bantamweight | 104.6 | 56.2% | 57.3% | 2.3 | 0.1 |
| 490 | Luana Carolina | Women's Bantamweight | 104.6 | 56.1% | 55.1% | 4.6 | 0.0 |
| 491 | Jake Hadley | Bantamweight | 104.6 | 56.1% | 56.7% | 3.9 | 0.0 |
| 492 | Marvin Vettori | Middleweight | 104.5 | 56.0% | 55.7% | 5.9 | 0.0 |
| 493 | Ollie Schmid | Featherweight | 104.5 | 55.9% | 57.3% | 3.2 | 0.0 |
| 494 | Yi Sak Lee | Middleweight | 104.3 | 55.8% | 54.8% | 2.7 | 0.0 |
| 495 | Chase Hooper | Lightweight | 104.3 | 55.7% | 58.6% | 5.1 | 0.3 |
| 496 | Jordan Leavitt | Featherweight | 104.1 | 55.6% | 56.6% | 2.5 | 0.2 |
| 497 | Luis Saldana | Featherweight | 104.1 | 55.5% | 55.9% | 5.6 | 0.1 |
| 498 | Julia Polastri | Women's Strawweight | 104.1 | 55.4% | 58.9% | 6.2 | 0.0 |
| 499 | Melissa Mullins | Women's Bantamweight | 104.0 | 55.3% | 53.1% | 3.7 | 0.1 |
| 500 | Bekzat Almakhan | Bantamweight | 103.9 | 55.2% | 56.0% | 2.6 | 0.0 |
Analytics guide
CCR
What it calculates
Current Competitiveness Rating (CCR): a composite score—like an Elo rating in chess—that evaluates advanced striking, grappling, pace, and opponent-quality stats into one number.
Why it matters
Weights recent performance much more heavily than older fights and incorporates factors like age. Use it to see who is fighting at an elite level right now.
TSR
What it calculates
True Skill Rating (TSR): the same composite approach applied across a fighter’s full UFC body of work, like an Elo rating built from advanced stats.
Why it matters
Does not overweight recency or adjust for age—it is a pure career evaluative metric. Helpful for separating sustained elite careers from short hot streaks.
SLpM
What it calculates
Total Significant Strikes Landed ÷ Total Fight Time (minutes)
Why it matters
Captures offensive pace and sustained scoring output — high SLpM forces opponents to fight at your tempo.
SApM
What it calculates
Total Significant Strikes Absorbed ÷ Total Fight Time (minutes)
Why it matters
Shows defensive efficiency and risk exposure — low SApM usually means better defense, distance management, and fewer damaging exchanges.
KD/M
What it calculates
Total Knockdowns ÷ Total Fight Time (minutes)
Why it matters
Measures a fighter’s ability to create high-impact, fight-changing moments that swing rounds or lead to finishes.
TD/M
What it calculates
Total Takedowns Landed ÷ Total Fight Time (minutes)
Why it matters
Signals wrestling pace and phase control — fighters who can reliably force grappling exchanges can neutralize dangerous strikers.
CTRL
What it calculates
Total Control Time ÷ Total Fight Time (minutes)
Why it matters
Quantifies positional dominance and round-winning leverage — control time often correlates with judges’ scoring and opponent suppression.
SUB/M
What it calculates
Total Submission Attempts ÷ Total Fight Time (minutes)
Why it matters
Reflects finishing threat on the mat — frequent submission attacks force defensive reactions that open up control and damage.
Rev
What it calculates
Total Reversals ÷ Total Fight Time (minutes)
Why it matters
Highlights scrambling and escape ability — reversals turn bad positions into scoring opportunities and can flip momentum.
C/15
What it calculates
(Total Control Time ÷ Total Fight Time) × 15 minutes
Why it matters
Standardizes grappling control to a 15-minute baseline so fighters with different bout lengths can be compared fairly.
TOP/15
What it calculates
(Total Top Control Time ÷ Total Fight Time) × 15 minutes
Why it matters
Separates high-quality control (top position) from other control contexts — strong top control is a reliable path to winning rounds.
CLN/15
What it calculates
(Total Clinch Control Time ÷ Total Fight Time) × 15 minutes
Why it matters
Measures fence control and pace management — clinch time can mute opponent offense and bank scoring minutes.
GRD/15
What it calculates
(Total Ground Control Time ÷ Total Fight Time) × 15 minutes
Why it matters
Shows takedown follow-through and top pressure — ground control often creates long stretches of safe scoring and attrition.
CAGE
What it calculates
(Total Cage Control Time ÷ Total Fight Time) × 15 minutes
Why it matters
Quantifies time spent driving position against the fence — useful for understanding style matchups and opponent disruption.
BACK
What it calculates
(Total Back Control Time ÷ Total Fight Time) × 15 minutes
Why it matters
Back control is premium dominance — it strongly predicts submission threat and long, uncontested control sequences.
CHIN
What it calculates
Model score (0–100): durability under damaging strikes
Why it matters
A quick durability signal — higher scores suggest a fighter can survive danger and remain competitive deep into fights.
TDAtt
What it calculates
Career total takedown attempts (UFC bouts)
Why it matters
Represents grappling intent and game-planning tendency — high attempt totals usually indicate a wrestler-first approach.
TDLnd
What it calculates
Career total takedowns landed (UFC bouts)
Why it matters
Proof of conversion and control potential — landed takedowns often precede control time, damage, and submission attempts.
TDAg
What it calculates
Career total opponent takedown attempts faced (UFC bouts)
Why it matters
Shows how often opponents choose to wrestle them — a proxy for perceived defensive striking threat or exploitable takedown defense.
TDDef
What it calculates
Career total opponent takedown attempts defended (UFC bouts)
Why it matters
Highlights defensive wrestling volume — strong defense keeps fights in a preferred phase and preserves striking efficiency.