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.
Eddie Wineland
Bantamweight
83.3
CCR
Chris Daukaus
Light Heavyweight
83.3
CCR
Ji Yeon Kim
Women's Flyweight
83.3
CCR
Michel Pereira
Middleweight
83.2
CCR
Harry Hunsucker
Light Heavyweight
83.1
CCR
Daniel Frunza
Welterweight
83.1
CCR
Ike Villanueva
Light Heavyweight
83.1
CCR
Dalcha Lungiambula
Middleweight
83.0
CCR
Kyle Prepolec
Lightweight
82.9
CCR
Misha Cirkunov
Light Heavyweight
82.8
CCR
Miranda Granger
Women's Strawweight
82.7
CCR
Felice Herrig
Women's Strawweight
82.7
CCR
Maki Pitolo
Middleweight
82.7
CCR
Shamil Abdurakhimov
Heavyweight
82.7
CCR
Saimon Oliveira
Bantamweight
82.6
CCR
Jordan Wright
Light Heavyweight
82.6
CCR
Chase Sherman
Heavyweight
82.5
CCR
Gerald Meerschaert
Middleweight
82.1
CCR
Frank Camacho
Lightweight
81.9
CCR
Mohammad Yahya
Featherweight
81.9
CCR
Andrei Arlovski
Heavyweight
81.9
CCR
Lukasz Brzeski
Heavyweight
81.8
CCR
Sam Alvey
Middleweight
81.8
CCR
Westin Wilson
Featherweight
81.7
CCR
Tony Ferguson
Welterweight
81.7
CCR
Emily Whitmire
Women's Flyweight
81.7
CCR
Charlie Ontiveros
Lightweight
81.4
CCR
Alexander Gustafsson
Light Heavyweight
81.4
CCR
Jesse Ronson
Lightweight
81.4
CCR
Marion Reneau
Women's Bantamweight
81.3
CCR
Bryan Barberena
Middleweight
81.3
CCR
Austen Lane
Light Heavyweight
81.2
CCR
Sean Soriano
Featherweight
81.2
CCR
Wu Yanan
Women's Bantamweight
81.1
CCR
Tim Means
Welterweight
81.0
CCR
Donald Cerrone
Welterweight
80.9
CCR
Sabina Mazo
Women's Flyweight
80.9
CCR
Edson Barboza
Lightweight
80.9
CCR
Ricky Glenn
Welterweight
80.8
CCR
Karl Roberson
Light Heavyweight
80.7
CCR
JP Buys
Bantamweight
80.7
CCR
Kevin Christian
Light Heavyweight
80.5
CCR
Gina Mazany
Women's Flyweight
80.5
CCR
Leah Letson
Women's Featherweight
80.3
CCR
Braxton Smith
Heavyweight
80.3
CCR
Andre Fialho
Welterweight
80.1
CCR
Lina Lansberg
Women's Bantamweight
80.0
CCR
Diana Belbita
Women's Flyweight
79.9
CCR
Jamie Pickett
Middleweight
79.4
CCR
Gian Villante
Heavyweight
79.3
CCR
| Rank | Fighter | Weight class | CCR | Percentile | Percentile per weight class | SLpM | TD / min |
|---|---|---|---|---|---|---|---|
| 1051 | Eddie Wineland | Bantamweight | 83.3 | 5.8% | 2.0% | 3.1 | 0.0 |
| 1052 | Chris Daukaus | Light Heavyweight | 83.3 | 5.7% | 11.7% | 3.8 | 0.0 |
| 1053 | Ji Yeon Kim | Women's Flyweight | 83.3 | 5.7% | 10.5% | 6.7 | 0.0 |
| 1054 | Michel Pereira | Middleweight | 83.2 | 5.6% | 10.4% | 4.3 | 0.0 |
| 1055 | Harry Hunsucker | Light Heavyweight | 83.1 | 5.5% | 10.4% | 2.4 | 0.0 |
| 1056 | Daniel Frunza | Welterweight | 83.1 | 5.4% | 5.0% | 2.5 | 0.0 |
| 1057 | Ike Villanueva | Light Heavyweight | 83.1 | 5.3% | 9.1% | 1.8 | 0.0 |
| 1058 | Dalcha Lungiambula | Middleweight | 83.0 | 5.2% | 9.6% | 4.4 | 0.1 |
| 1059 | Kyle Prepolec | Lightweight | 82.9 | 5.1% | 4.1% | 3.3 | 0.0 |
| 1060 | Misha Cirkunov | Light Heavyweight | 82.8 | 5.0% | 7.8% | 3.3 | 0.3 |
| 1061 | Miranda Granger | Women's Strawweight | 82.7 | 4.9% | 2.7% | 2.1 | 0.0 |
| 1062 | Felice Herrig | Women's Strawweight | 82.7 | 4.8% | 1.4% | 6.1 | 0.0 |
| 1063 | Maki Pitolo | Middleweight | 82.7 | 4.8% | 8.7% | 2.8 | 0.0 |
| 1064 | Shamil Abdurakhimov | Heavyweight | 82.7 | 4.7% | 10.5% | 2.2 | 0.0 |
| 1065 | Saimon Oliveira | Bantamweight | 82.6 | 4.6% | 1.3% | 2.4 | 0.0 |
| 1066 | Jordan Wright | Light Heavyweight | 82.6 | 4.5% | 6.5% | 3.2 | 0.2 |
| 1067 | Chase Sherman | Heavyweight | 82.5 | 4.4% | 9.2% | 6.1 | 0.0 |
| 1068 | Gerald Meerschaert | Middleweight | 82.1 | 4.3% | 7.8% | 2.7 | 0.1 |
| 1069 | Frank Camacho | Lightweight | 81.9 | 4.2% | 3.4% | 4.6 | 0.0 |
| 1070 | Mohammad Yahya | Featherweight | 81.9 | 4.1% | 3.5% | 3.5 | 0.0 |
| 1071 | Andrei Arlovski | Heavyweight | 81.9 | 4.0% | 7.9% | 4.7 | 0.0 |
| 1072 | Lukasz Brzeski | Heavyweight | 81.8 | 3.9% | 6.6% | 4.7 | 0.0 |
| 1073 | Sam Alvey | Middleweight | 81.8 | 3.9% | 7.0% | 3.9 | 0.0 |
| 1074 | Westin Wilson | Featherweight | 81.7 | 3.8% | 2.8% | 1.6 | 0.1 |
| 1075 | Tony Ferguson | Welterweight | 81.7 | 3.7% | 4.3% | 4.0 | 0.0 |
| 1076 | Emily Whitmire | Women's Flyweight | 81.7 | 3.6% | 8.8% | 1.2 | 0.5 |
| 1077 | Charlie Ontiveros | Lightweight | 81.4 | 3.5% | 2.8% | 2.8 | 0.0 |
| 1078 | Alexander Gustafsson | Light Heavyweight | 81.4 | 3.4% | 5.2% | 4.5 | 0.0 |
| 1079 | Jesse Ronson | Lightweight | 81.4 | 3.3% | 2.1% | 3.6 | 0.0 |
| 1080 | Marion Reneau | Women's Bantamweight | 81.3 | 3.2% | 6.1% | 2.1 | 0.0 |
| 1081 | Bryan Barberena | Middleweight | 81.3 | 3.1% | 6.1% | 5.2 | 0.0 |
| 1082 | Austen Lane | Light Heavyweight | 81.2 | 3.0% | 3.9% | 2.5 | 0.1 |
| 1083 | Sean Soriano | Featherweight | 81.2 | 3.0% | 2.1% | 2.3 | 0.1 |
| 1084 | Wu Yanan | Women's Bantamweight | 81.1 | 2.9% | 4.1% | 3.7 | 0.0 |
| 1085 | Tim Means | Welterweight | 81.0 | 2.8% | 3.6% | 4.9 | 0.2 |
| 1086 | Donald Cerrone | Welterweight | 80.9 | 2.7% | 2.9% | 3.2 | 0.0 |
| 1087 | Sabina Mazo | Women's Flyweight | 80.9 | 2.6% | 7.0% | 4.2 | 0.0 |
| 1088 | Edson Barboza | Lightweight | 80.9 | 2.5% | 1.4% | 4.1 | 0.0 |
| 1089 | Ricky Glenn | Welterweight | 80.8 | 2.4% | 2.2% | 2.2 | 0.1 |
| 1090 | Karl Roberson | Light Heavyweight | 80.7 | 2.3% | 2.6% | 1.5 | 0.0 |
| 1091 | JP Buys | Bantamweight | 80.7 | 2.2% | 0.7% | 1.0 | 0.1 |
| 1092 | Kevin Christian | Light Heavyweight | 80.5 | 2.2% | 1.3% | 4.3 | 0.0 |
| 1093 | Gina Mazany | Women's Flyweight | 80.5 | 2.1% | 5.3% | 5.9 | 0.1 |
| 1094 | Leah Letson | Women's Featherweight | 80.3 | 2.0% | 33.3% | 1.2 | 0.1 |
| 1095 | Braxton Smith | Heavyweight | 80.3 | 1.9% | 5.3% | 6.5 | 0.0 |
| 1096 | Andre Fialho | Welterweight | 80.1 | 1.8% | 1.4% | 3.3 | 0.0 |
| 1097 | Lina Lansberg | Women's Bantamweight | 80.0 | 1.7% | 2.0% | 2.5 | 0.0 |
| 1098 | Diana Belbita | Women's Flyweight | 79.9 | 1.6% | 3.5% | 6.6 | 0.0 |
| 1099 | Jamie Pickett | Middleweight | 79.4 | 1.5% | 5.2% | 2.5 | 0.1 |
| 1100 | Gian Villante | Heavyweight | 79.3 | 1.4% | 3.9% | 4.1 | 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.