Tee Higgins

WR · CIN Active

Age

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Experience

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Height

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Weight

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College

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	Season	Career
Fantasy
Points per game	—	—
WR stats
Target share	—	—
Snap share	—	—
Touchdowns	—	—

Higgins remains a clear secondary target behind the top option, with 2025 target share at 18% (81st percentile at the position) and a strong 89th-percentile points-per-opportunity of 2.16, showing he converts limited looks efficiently even as target share slipped 3 points below his career median; the three-season slope is positive on ppg (2.64) and points-per-opportunity (0.35), signaling improving efficiency despite fewer targets, though snap share trended down (-0.122) into last season's finale. ESPN's 13.0-point week projection looks conservative against last game's 18.7 ppg, 87% snap share, and 55% air-yards share, none of which the projection appears to fully credit. Heel concern is resolved: full practices Wednesday-Friday and confirmed as playing. Watch snap share and air-yards share for early signs of role expansion in a 27.0-implied-point game.

This Week

5.2 to 20.7, most likely 13.0

Season: 132.0 to 245.1, most likely 188.5

Usage

Role and Availability

Secondary target (target share 0.16, air-yards share 0.38).

2025: played 15 of 17, no multi-game absences; 2024: played 12 of 17, 2 multi-game absences.

2025: WR2 of 6 on CIN by total points.

What Chuck Weighed

• Usage stickiness (how well shares of targets, carries and snaps persist, by position and horizon) (90% sure)
• Rookie-year finish as the year-2 and year-3 breakout signal (WR and RB, with target share, TE and QB) (68% sure)
• Situational factors (wind, cold, dome, rest, Vegas implied total and spread) (68% sure)
• Draft capital as a breakout prior (early-career top-24 rates by round for WR and RB, top-12 for TE and QB) (65% sure)
• Touchdown regression (how much of TD rate is noise, expected TDs from opportunity location, QB included) (65% sure)
• Ball-share shifts (what moves when a teammate is absent or the QB changes, how fast, how long it sticks) (64% sure)
• Processing injury news and practice reports (62% sure)
• Team-change discount (how much prior-season usage share survives a move, and why a QB change alone is not a discount) (62% sure)
• Player range of outcomes (distributions, injury base rates, age curves) (61% sure)
• Bye weeks and fantasy playoff schedule planning (60% sure)
• Weekly lineup optimization (60% sure)
• No-prior PPG shrink (what a rookie or a player without a qualifying prior season shrinks his season-to-date points toward, and how many pseudo-games the draft-capital prior is worth) (60% sure)
• Window length of the in-season PPG shrink (the whole season to date beats the last four games; recency weighting, a week term and a points-scale fit add nothing) (60% sure)
• PPG-level in-season shrink (how much of a trailing-4-game points ratio carries into the next game, and whether it beats the raw average and the share blend for projecting points) (60% sure)
• How many games of usage to trust (the in-season window) (60% sure)
• Scoring distribution shape (weekly spread around expectation, next-season PPG spread, by position and tier) (60% sure)
• Team range of outcomes (season simulation, playoff and title odds) (60% sure)
• Correlation and stacking between roster players (60% sure)
• Football probability base rates (variance, TD regression, sticky stats, game environment) (60% sure)
• Quarterback interception regression (season INT rate is noise; volume and depth mix are what carry) (60% sure)
• In-season efficiency as a points trigger (a weeks 1-6 YPC or yards-per-target fall adds nothing to the trailing points average; measured null) (55% sure)
• Pseudo-game blend of the in-season PPG shrink (the prior season is worth about 4-5 games, but blending by games played adds nothing to the fixed season-to-date line; measured null) (55% sure)
• Signals inside a draft-round tier (pick slot within round 1 and age at NFL entry add nothing to round; measured null) (50% sure)
• QB quality and receiver points (how much a passer's quality moves his receivers' points, and why a new passer's prior season predicts none of it) (50% sure)
• Veteran decline signals (age by position, early-season snap-share drift, efficiency proxies) (25% sure)
• Soft breakout signals (camp reports, preseason usage, college breakout age, age at breakout) (not yet measured)
• Coaching and play-caller change (how much of last season's share survives a new offense) (not yet measured)

Updated yesterday 6:05 pm ET