NFL TD Prop Record Keeping: How to Track Your Bets and Measure Your Edge

I didn’t start tracking my TD bets until my third season, and I consider those first two years largely wasted as a result. Not because the bets were bad — some of them were probably fine — but because without records, I had no way to know which parts of my process were working and which were leaking value. Memory is unreliable. You remember the big wins and the painful losses but forget the fifty ordinary bets in between that actually determine your season-long performance. A spreadsheet doesn’t forget anything, and that objectivity is the foundation everything else is built on.

What Data to Record for Every TD Bet

The minimum viable record for each TD bet needs to capture enough information to evaluate the decision, not just the outcome. A simple win/loss log tells you almost nothing useful, because a winning bet placed at bad odds is still a bad decision, and a losing bet placed at good odds is still a good one. The data needs to separate process quality from outcome quality.

For every TD prop I place, I record the date, player name, bet type (anytime, first, last, or 2+), the decimal odds, the stake, the bookmaker, and the result. That’s the bare minimum. On top of that, I add three analytical fields: the player’s TD rate over his last eight games at the time of the bet, the implied probability of the odds, and whether the bet represented positive expected value at the time of placement. These three fields transform the log from a results tracker into a process evaluator.

The additional fields take about 30 seconds per bet to fill in, and they’re the most valuable data in the entire spreadsheet. When I review my results at the end of a season, the first thing I check isn’t my overall profit — it’s my positive-EV classification accuracy. If I correctly identified 70% or more of my bets as positive EV, and those bets hit at a rate consistent with their expected probability, my process is sound even if the total profit is modest. If my positive-EV bets are hitting at a rate significantly below expectation, either my probability estimates are wrong or I’m experiencing a variance run that will eventually correct.

Calculating ROI and Yield on TD Props

Two metrics define your TD betting performance: return on investment (ROI) and yield. ROI measures your total profit relative to your total stakes. Yield measures your average profit per bet. Both matter, but they tell different stories. The average American bettor stakes roughly $335 per NFL season — for UK punters with a disciplined approach, the stakes may be higher, but the ROI calculation works the same way regardless of volume.

ROI is calculated as total profit divided by total stakes, expressed as a percentage. If you staked £1,000 across the season and finished with £1,050 in returns, your ROI is 5%. Yield divides total profit by the number of bets — if that £50 profit came from 100 bets, your yield is £0.50 per bet, or 0.5% per unit staked.

For TD props specifically, a positive ROI over a full season is the goal but not the expectation in year one. The bookmaker’s margin on TD props ranges from 5-10%, which means you need to overcome that margin through better-than-market probability assessment. A realistic target for a disciplined TD prop bettor with a sound process is 2-5% ROI over a full season. That doesn’t sound exciting, but applied to meaningful stakes over multiple seasons, it represents a genuine and sustainable edge.

Track ROI by bet type as well as overall. You may discover that your anytime TD bets are profitable but your first TD bets are not, or that your 2+ TD plays show strong results while your standard anytime plays break even. This granularity lets you double down on what’s working and cut what isn’t, which is impossible without detailed records. For understanding how your closing odds compare to your opening bets, the relationship with closing line value provides another layer of process measurement.

How Many Bets Before You Can Trust Your Results

This is the question that separates serious bettors from recreational ones. The honest answer is uncomfortable: you need far more bets than most people think before your results are statistically meaningful. With 20% of American adults betting on sports and a growing proportion of UK adults doing the same, the vast majority evaluate their “edge” on sample sizes too small to distinguish skill from luck.

For TD props with an average implied probability around 40-50%, you need a minimum of 200 bets before the results begin to separate signal from noise. At 200 bets, a genuine 3% edge will start to show up as a positive trend, but the confidence interval is still wide enough that a breakeven or slightly negative result doesn’t disprove the edge. At 500 bets, the picture sharpens considerably. At 1,000 bets, you can be reasonably confident that your results reflect your true skill level rather than variance.

A typical NFL season offers 17 weeks of regular-season action plus playoffs. If you place three to five TD bets per week, that’s 50-85 bets per season. Reaching 200 bets takes roughly three seasons of consistent, disciplined betting. This timeline frustrates punters who want immediate feedback on their process, but it’s the mathematical reality. Anyone claiming to have a proven TD prop edge after one season of 60 bets is working with a sample too small to prove anything.

The practical implication is patience. Keep detailed records from day one, review them quarterly, look for process signals rather than outcome signals, and resist the urge to overhaul your approach based on a 30-bet sample. The edge, if it exists, will reveal itself over time — but only if the records are comprehensive enough to capture it.

One shortcut that helps while you’re building your sample: benchmark your bets against the closing line rather than just the outcome. If your pre-game odds consistently represent value relative to what the line closes at before kick-off, that’s a process signal that emerges faster than win-rate analysis. You might only need 50-100 bets to identify a consistent closing-line advantage, compared to 200+ for outcome-based evaluation. This interim metric gives you useful feedback within a single season while the longer-term hit rate data accumulates.