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Brodrick Malone

Brodrick Malone

Brodrick Malone is a Versatile WR for New Mexico State.

What projects, and what doesn't · WRs · held out 2019-2025

How well one season predicts the next, measured on seasons the model never trained on. 1.00 would be perfectly predictable; 0.00 means last year told us nothing.

  • Usage share (volume)0.47
    Beats guessing the WR average by 16%. n=6,302 WR seasons
  • EPA per play (efficiency)0.09
    Not projectable — we do not forecast this. n=5,767 WR seasons
  • Total EPA (value)0.45
    Beats guessing the WR average by 12%. n=5,767 WR seasons

Across all positions, usage carries year to year at 0.47 and efficiency at 0.07. So Brodrick Malone's projection is a projection of opportunity — how much of the offense he runs through. How well he converts it is something this model does not claim to know a year in advance, and the number above is why.

2025 Production

Receiving
22 Receptions242 Rec yards2 Rec TD11.0 Yards/rec

Air yards

Where the ball goes, and how much of the gain is the throw rather than the run after it.

10
Targets
14.0y
Avg depth
3.3y
After catch
60%
Catch rate

The ball travels 14.0 yards in the air on an average target 5.0 yards deeper than the median. Another 3.3 comes after the catch.

Where he is targeted

targets · completion rate · yards
deep
2
0% · 0y
1
100% · 33y
0
short
5
60% · 23y
1
100% · 20y
1
100% · 20y
leftmiddleright

Depth and direction come from CFBD’s passing detail, which is backfilled after the games: this covers weeks 1–2 only. Spikes, throwaways and intentional grounding are excluded before any average.

Performance Analysis · 2025 · vs WR peers

  • Efficiency100
  • Volume11
  • Explosiveness27
  • Consistency49
  • Pass-Down72
Player type
Versatile WR

Balanced profile without a single dominant trait — contributes across multiple dimensions.

Balanced usageMulti-role
Peer percentiles
Opponent-adjusted EPA (WEPA/play)100th %ile · elite
Game-to-game consistency49th %ile · average
Key findings
  • Top-10% efficiency among WRs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Peak game: 3.24 EPA/play in Wk 3 vs Louisiana Tech (SP+ -1).

NIL Market Tier· 2025

On3 valuation ↗
Star

Top-10 nationally. Multiple mid-to-large collective deals expected.

Tier is a model estimate based on position, school brand, performance rank, and usage — not a reported deal. NIL deals are private. For a real market valuation, see On3's NIL profile, which factors in social following and actual deal tracking.

Historical Comparables · WR · efficiency + volume + value

Players from 2013–2025 matched on EPA efficiency, play volume, and adjusted value tier — not just one metric.

PlayerTeamWEPA/playLine valTotal EPA
Tyler BuchnerNotre Dame0.2500.312.8
Keytaon ThompsonVirginia0.3200.412.5
Nick NashSan José State0.3100.514.0
Brandon ChatmanNavy0.2300.612.0
Samajie GrantArizona0.2500.315.5

Comps are statistical — efficiency, volume, and value tier all factor in. Style and conference context differ.

Game Log · box score + EPA, week by week

+3.2403.24Wk 1 vs Bryant: +1.03 EPA/play1Wk 2 vs Tulsa: +0.24 EPA/play2Wk 3 vs Louisiana Tech: +3.24 EPA/play3Wk 5 vs New Mexico: -0.95 EPA/play5
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1vsBryantW19-3188.0081.03
2vsTulsaW21-14-10.03196.3070.24
3@Louisiana TechL14-49-1.314141.01413.24
5@New MexicoL20-380.9144.004-0.95
8@LibertyL27-30-9.023718.5120
11vsKennesaw StateL21-24-5.433612.0023
12@TennesseeL9-4215.03134.308
13@UTEPW34-31-17.555511.0023
14vsMiddle TennesseeL24-31-16.03299.7011

Usage & Situational · Pro

Snap-share proxy
Overall
3.8%
Passing plays
6.1%
Rushing plays
0.0%
Standard downs
5.3%
Passing downs
1.7%
EPA by down type
Standard downs
0.24
Passing downs
0.37
Pass / Rush EPA
0.26 / —

Usage = share of team plays (CFBD has no true snap counts).

EPA = expected points added (opponent-adjusted). NIL estimates are model-based ranges, not reported deals. Data: CollegeFootballData. Not betting advice.