Positive EV TD Props: How to Calculate Expected Value on Touchdown Markets
Available in US
Most TD Prop Guides Mention Value Without Showing You How to Find It
I read at least a dozen NFL TD prop strategy articles before I placed my first serious wager, and every single one told me to “find value” without explaining what that meant mathematically. “Look for value” is the betting equivalent of a cooking recipe that says “season to taste” — technically correct but useless if you do not know what the food is supposed to taste like.
Sportsbooks dedicate far more resources to setting accurate spreads and totals than they do to pricing individual player props. That disparity — identified by TheLines and confirmed by my own tracking — is exactly why TD props represent a fertile ground for expected value analysis. The less attention the sportsbook pays to a market, the more likely it is to contain pricing errors that a disciplined bettor can exploit. But exploiting those errors requires a framework, not just a feeling.
Expected value is that framework. It is the one metric that tells you whether a bet will be profitable over hundreds of repetitions, regardless of whether it wins or loses on any single occasion. If you leave this page with only one concept, let it be this: a positive expected value bet is one where the true probability of winning exceeds the implied probability built into the odds. Everything else in TD prop strategy flows from that single idea.
The Expected Value Formula Applied to TD Props
The EV formula is not complicated, but it demands honest inputs. Here it is:
EV = (Probability of Winning x Profit if Win) – (Probability of Losing x Stake Lost)
Suppose you estimate that a running back has a 38% chance of scoring anytime in a given game. His odds are 2.50 in decimal format. For a one-unit stake, the calculation runs: (0.38 x 1.50) – (0.62 x 1.00) = 0.57 – 0.62 = -0.05. That bet has a negative expected value of -0.05 units per bet. Over 100 identical bets, you would expect to lose about five units.
Now imagine the same player at odds of 3.00. The calculation becomes: (0.38 x 2.00) – (0.62 x 1.00) = 0.76 – 0.62 = +0.14. Positive expected value of 0.14 units per bet. Over 100 repetitions, you expect to profit roughly fourteen units. The player, the matchup, and the probability are identical — only the price changed, and with it, the entire value proposition of the bet.
The formula reveals something that casual bettors often miss: value is not about which player will score. It is about whether the price offered exceeds the fair price for that outcome. A player with a 25% chance of scoring at odds of 5.00 (implied probability 20%) is a better long-term bet than a player with a 40% chance at odds of 2.20 (implied probability 45.5%). The first bet has positive EV; the second has negative EV, despite the second player being more likely to actually score.
Estimating True Touchdown Probability from Data
The EV formula is only as good as your probability estimate. Garbage in, garbage out. This is where analytical work separates profitable TD prop bettors from everyone else.
My probability estimates start with base rates. Anytime TD bets for leading running backs and wide receivers hit at 25-40% depending on role and offensive context. That range is the starting point, not the answer. From there, I adjust upward or downward based on four factors.
First, red-zone usage. In the 2024 season, 77.2% of all NFL touchdowns came from the red zone. A player’s share of his team’s red-zone touches is the strongest single predictor of his touchdown probability. If a running back receives 65% of his team’s goal-line carries and the team averages four red-zone trips per game, his per-game TD probability is materially higher than a back with 30% of goal-line carries on the same team.
Second, receiving TDs account for roughly 65% of all NFL touchdowns. For wide receivers and tight ends, red-zone target share replaces carry share as the primary predictor. The calculation is analogous — share multiplied by volume equals expected red-zone opportunities per game.
Third, the defensive matchup adjusts the base probability. A defence allowing touchdowns on 65% of red-zone possessions inflates TD probability for all opposing skill players. A defence like the Denver Broncos in 2025, who allowed the lowest red-zone TD rate in the league at 42.6%, compresses it. I apply a multiplier of roughly 0.85 to 1.15 depending on where the opposing defence ranks.
Fourth, game script and total. Higher projected game totals imply more touchdowns for both teams. I use the Vegas total as a proxy: each point above 45 adds approximately 0.5-1% to my base TD probability estimate for primary scorers, and each point below 45 subtracts similarly.
The resulting estimate is imperfect. It always will be. But an imperfect estimate built from relevant data beats both blind guessing and the sportsbook’s implied probability, which is distorted by margin and public money flow.
Closing Line Value: Measuring Your Edge Over Time
How do you know if your EV estimates are actually accurate? You cannot judge by results alone — variance in a 30-40% hit rate market means you can run well or poorly for weeks without it reflecting your true skill. This is where closing line value becomes essential.
CLV measures whether the odds you took were better than the final closing odds at kickoff. If you bet a player at 3.00 on Wednesday and the line closes at 2.60 on Sunday, you captured positive CLV — the market moved toward your position, suggesting your early assessment was correct. If you bet at 3.00 and the line closes at 3.40, you took a worse price than the market eventually settled on, indicating your assessment may have been off.
Over a sample of fifty or more bets, consistent positive CLV is the strongest evidence that your probability estimates are adding genuine edge. Even if short-term results show a loss, positive CLV means you are betting into the right side of the market. Results and CLV tend to converge over larger samples, but CLV gets there faster because it eliminates the noise of individual outcomes.
I track CLV on every TD prop I place by recording both my entry odds and the closing odds at kickoff. After each four-week block, I calculate the average CLV across all bets. Anything above zero tells me the approach is working. Anything consistently below zero means I need to reassess either my data sources, my probability model, or my timing. This tracking system, combined with a robust strategic framework, is what transforms TD prop betting from entertainment into a disciplined analytical practice.
What is positive expected value in TD prop betting?
Positive expected value means a bet will be profitable over many repetitions because the true probability of winning exceeds the implied probability built into the odds. A +EV TD prop is one where your estimated chance of the player scoring is higher than what the sportsbook’s odds suggest. Finding +EV bets consistently requires estimating true touchdown probabilities from data rather than relying on intuition.
How reliable is closing line value as a performance metric for TD props?
CLV is the most reliable intermediate performance metric for TD prop betting. Over a sample of at least fifty bets, consistent positive CLV indicates that your probability estimates are more accurate than the market’s final assessment. Short-term results can be noisy due to the 25-40% hit rate of TD props, but CLV filters out that variance by measuring whether you consistently beat the closing price.
Created by the ”nfl td Prop Bets” editorial team.
