Every player
New here?

How to read this page

▶

Team tables split by side of the ball, and the Side control hides the half you are not reading. What a team allowed is what its opponents gained, so the allowed tables are the offensive ones read from the other end. The roster is everyone who recorded a stat line, because the source holds no roster of its own — a player who never got on the field is absent. A dash means no number for that cell, or a zero in a roster column that counts.

CLT

Joachim Bangda

Joachim Bangda comes in 915th of 1008 ncaaf rbs in fumbles, at 3.

94th beats 94% of the NCAAF RBs; 1 is the best of however many the row says. Higher is always better, whichever way the stat runs. Red to green by where the placing sits. A stat with no better end is left unpainted.

all 5 shown▶
Playing Timevs NCAAF RBs▶
Games Played8
EfficiencyOffensevs NCAAF RBs▶
Carries with a Countable Outcome73
Points Added per Play0.0727th271/373
Total Points Added5.247th520/986
Success Rate Running37%17th308/372
RushingOffensevs NCAAF RBs▶
Carries9.467th326/996
Rushing Yards45.367th333/996
Yards per Carry4.847th264/494
Rushing Touchdowns249th448/996
Longest Run3352nd471/996
ReceivingOffensevs NCAAF RBs▶
Receptions0.13rd878/904
Receiving Yards1.815th767/904
Yards per Catch14.0—
Receiving Touchdowns033rd300/904
Longest Catch1433rd584/904
Ball SecurityOffensevs NCAAF RBs▶
Fumbles36th915/1008
Fumbles Lost070th1/1008
8 games
DateOppCARRUSH YDSRUSH TDLONG RUNRECREC YDSREC TDFUM
TOWNov 19, 2022HAMPHAMP1181027———1
TOWNov 12, 2022@STBKSTBK6204———1
TOWNov 5, 2022VILLVILL17107033———0
TOWOct 29, 2022@MONMMONM125622011400
TOWOct 22, 2022W&MW&M1157021———1
TOWOct 8, 2022@ELONELON3001———0
TOWOct 1, 2022@DELDEL1253014———0
TOWSep 17, 2022@WVUWVU3603———0
By season3 seasons
SeasonGPCARRUSH YDSYDS/CARRUSH TDLONG RUNRECREC YDSREC TD
2023CLT8421433.44315460
2022TOW8753624.82331140
2021KENT4191357.1160160

About College Football Research

▶

Every number the site holds for a college football team and the players who played for it, in one place. Pick a team, a season and a set of games, and read it. Nothing here needs an account.

Where the numbers come from

The same tables the boards read, resolved through one shared definition per stat. A rate is computed over the whole window rather than averaged across games, so a season figure is not the mean of the nightly ones.