NRFI, or No Run First Inning, is a focused betting market that resolves on the first inning's runs. This guide shows how to translate public first inning scoring data into a practical NRFI probability and how to refine that baseline with pitcher, park, and weather inputs. We present a stepwise workflow you can use before making a single NRFI estimate, with links to the public tables and a few worked examples to make the math concrete.

NRFI hit rate equals one minus the first inning scored percentage, so public first inning tables give a direct baseline.

Adjust the league baseline with team first inning rates, starter first-inning splits, park factor, and conservative weather tweaks.

Document inputs and track results over time to test whether your NRFI model produces a persistent edge.

What NRFI means and how hit rate is defined

No Run First Inning, commonly abbreviated NRFI, is a simple bet: it wins when no runs score in the top and bottom of the first inning. In practical terms an NRFI bet covers both teams failing to score in that opening frame, so the event is binary and straightforward to check in the box score. For a concise, reader-friendly primer on what an NRFI wager is and when it settles, see a basic explanation on NRFI betting from a public guide.

Because NRFI is defined by the absence of any first inning scoring, its hit rate is the exact complement of the first inning scored percentage. That identity lets you convert reported first inning scoring rates directly into NRFI probabilities. For a clear explanation of how NRFI outcomes relate to first inning scoring percentages, consult a public NRFI overview.

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Use the simple identity between first inning scoring and NRFI as a baseline before adding matchup, pitcher, park, or weather adjustments.

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Put as a formula, if a league or matchup has a first inning scored percentage of S, then the NRFI hit rate is 1 minus S. For example, if S were 0.28, the NRFI baseline would be 0.72. You can find continuously updated first inning scored percentages to plug into this formula from TeamRankings' 1st inning tables.

How to calculate league and season NRFI rates from public data

Start by pulling the league-level 1st inning scored percentage from a current season table. TeamRankings maintains a season table that lists the first inning scored percentage by team and, aggregated, by league. Use the league aggregate S to compute the league NRFI baseline as 1 minus S. TeamRankings is the continuously updated source that lets you do this without relying on market prices.

Next, express the result as a percentage or probability for clarity. For example, if TeamRankings shows a league first inning scored percentage of 0.26, write NRFI baseline = 1 - 0.26 = 0.74, or a 74 percent chance of no runs in the first inning at the league level. Keep the arithmetic explicit so you can re-run it midseason as S changes.

Season-to-season shifts in run environment will change S and therefore the NRFI baseline. A higher overall scoring season will tend to reduce NRFI hit rates, while a lower run environment increases them. Because TeamRankings updates through the season, refresh S regularly to keep your baseline current.

Key public data sources and what each provides

Three public sources provide the core inputs you will use: TeamRankings for first inning percentages, Baseball-Reference for season scoring-by-inning context, and specialized leaderboards for pitcher splits and park effects. TeamRankings is the central place to get the baseline first inning scored percentage you need to compute NRFI rates.

Baseball-Reference publishes season summaries with scoring-by-inning tables that help you understand inning-to-inning patterns, for example that the first inning often has elevated scoring relative to other frames. That context is helpful when you compare league baseline values across multiple seasons.

Pull TeamRankings 1st inning table, FanGraphs splits, and Baseball Savant park factors

League baseline
Team offense
Team defense
Starter split
Park factor
Temperature

Use current season tables

FanGraphs provides inning-level pitcher splits that let you gauge a starter's historical first inning run prevention, while Baseball Savant supplies park factor leaderboards that quantify venue run environments. Together these three sources give the measurable inputs you need to move from a league baseline to a game-specific NRFI estimate.

Adjusting the baseline for team and matchup effects

To move from a league baseline to a matchup estimate, start with each team's first inning rates. Use a team's first inning for rate to measure its tendency to score in the first, and its first inning against rate to capture how often it concedes runs early. The match-level expectation comes from combining those two team figures in a transparent manner.

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A simple approach is to average the two team first inning scored percentages to form an adjusted first inning scoring estimate for the game, then take 1 minus that average to get the matchup NRFI baseline. For example, if Team A has a first inning scored rate of 0.22 and Team B has 0.30, the combined first inning scored percentage would be (0.22 + 0.30) / 2 = 0.26, and the NRFI baseline would be 0.74. TeamRankings' team-level tables give the raw rates needed for this step.

Home advantage and lineup construction matter: teams batting at home may face park effects that raise or lower first inning scoring, and the top of the lineup typically faces the opposition starter in the first inning which tends to concentrate scoring in that frame. Use the team-level data and consider simple weighting if you think one side's lineup strength or recent form should carry extra influence.

How starting pitcher first inning splits change NRFI odds

Starting pitchers can materially shift NRFI odds because some starters allow more runs in the first inning than their overall numbers suggest. Useful inning-level metrics to inspect are first-inning ERA and inning-level wOBA allowed from splits tables. FanGraphs maintains inning-level splits that let you compare a starter's first inning performance to league norms.

When a starter has a notably worse first inning track record, reduce the NRFI probability accordingly; when a starter routinely suppresses first inning runs, increase it. Be cautious with small sample sizes, because inning-level splits can be noisy over limited appearances. For specific inning-level split leaderboards use a FanGraphs splits lookup to see complete starter histories.

How often do NRFI bets hit?

NRFI bets hit at a rate equal to one minus the first inning scored percentage for the relevant league or matchup, adjusted for team splits, starter first-inning performance, park factors, and weather.

Also remember to check announced rotations and any bullpen usage notes: if a team plans an opener or has frequent early hooks, the first inning dynamics change and a pure starter split may be a poor predictor. Small samples and sudden role changes are common reasons to avoid overconfident adjustments based on first inning splits.

Park factors and venue adjustments for first inning scoring

Park factors quantify how a venue increases or decreases run scoring relative to a neutral park. Baseball Savant's park factor leaderboard provides season-level park multipliers you can use to scale a baseline first inning probability up or down depending on where the game is played.

In practice, apply a modest scaling to the combined matchup first inning scored percentage: if a park has a season park factor indicating a 5 percent higher run environment, increase the expected first inning scoring rate by a corresponding small amount before converting to NRFI. Use the Baseball Savant park factors table to pull the appropriate park adjustment for the current season.

Weather and temperature effects on first inning scoring

Temperature and certain wind conditions affect the carry of well-struck balls and the frequency of home runs, which in turn influence the probability of runs in the first inning. Studies and league reporting show that higher temperatures generally correlate with more home runs, so hot conditions merit a conservative increase to the expected first inning scoring risk.

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Before a game, check temperature and wind direction and consider a small upward adjustment to the first inning scored percentage in hot, outward-blowing conditions, and a small downward adjustment in cold or inward-blowing conditions. Treat these adjustments conservatively rather than making large, precise edits.

A step by step NRFI probability model you can run

Assemble these inputs in order: league baseline S from TeamRankings, team first inning for and against rates, the starting pitcher's first inning splits from FanGraphs, the venue park factor from Baseball Savant, and a weather adjustment for temperature and wind. Record each input so you can audit decisions later.

One practical formula is additive in log-odds space, but a simpler, transparent approach works for everyday use. Start with the matchup first inning scored percentage M (for example by averaging team rates), then apply multiplicative park and weather scalars and a starter adjustment factor, yielding an adjusted scored percentage M'. Finally compute NRFI probability as 1 - M'. Use TeamRankings for M, FanGraphs for the starter factor, and Baseball Savant for park effects.

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As a worked sample calculation, suppose TeamRankings gives a league baseline S of 0.27 and your team averaging yields a matchup M of 0.25. If the park factor suggests a 3 percent increase in scoring and the starter's first-inning tendency increases expected runs by a further 4 percent, combine them multiplicatively: M' = 0.25 * 1.03 * 1.04 = about 0.267. The NRFI probability becomes 1 - 0.267 = 0.733, or roughly 73.3 percent. Pull the league and starter numbers from TeamRankings and FanGraphs respectively when you run this example in practice.

Always quantify uncertainty: provide a confidence band around M' based on sample sizes for starter splits and recent roster changes. When sample sizes are small, widen the band and reduce conviction in any betting decision that depends on the point estimate.

Decision criteria: when an NRFI estimate looks like value

Compare your model probability to the market's implied probability from posted NRFI odds. Convert market odds to implied probability, account for vig, and then compare. If your estimate exceeds the implied probability by a practical margin that accounts for model error, you might consider the position worth further review.

Set a margin that reflects your confidence. For example, with moderate confidence and small sample adjustments, a margin of several percentage points may be sensible. If starter splits rest on tiny samples or weather uncertainty is high, require a larger margin before taking action. Public NRFI guides outline the basic idea of comparing model probability to market implied odds as the core value test.

Common mistakes and pitfalls to avoid

A common error is overweighting a single game's small-sample starter split. First inning splits can be volatile; a handful of poor first-inning outings will distort a starter's apparent tendency if you do not account for sample size. Check long-term splits and recent trends before making strong adjustments.

Another frequent mistake is ignoring lineup and roster changes that alter first inning probabilities. If a team promotes a new leadoff hitter or rests a regular early in the day, team first inning rates can shift quickly. Also avoid using stale season aggregates without confirming they still reflect the current roster and rotation mix.

Practical worked examples: three sample matchups

Example A, neutral park, league baseline heavy lift. Start with a matchup average M equal to the league-adjusted value from TeamRankings. If M calculates to 0.24 and the starter and park adjustments are negligible, NRFI estimate is 1 - 0.24 = 0.76. This shows how a league baseline dominated case yields a high NRFI probability when no other signals move the number materially.

Example B, pitcher with strong first inning splits. Suppose the matchup M is 0.26 on team averages but the starter's historical first-inning data show markedly fewer runs allowed than peers. Applying a starter adjustment downward to M to reflect that strength might reduce M to 0.22, and NRFI becomes 0.78. Use FanGraphs splits to justify a starter adjustment and to document sample size so you can judge the reliability of the shift.

Example C, hot weather and hitters at home. If the base matchup M is 0.25 but the park factor and temperature suggest a combined 7 percent lift to scoring, compute M' approximately 0.25 * 1.07 = 0.2675, giving NRFI ~ 0.7325. The temperature-driven increase is modest but meaningful; Baseball Savant park factors and weather checks provide the inputs you need to make that change credible.

Using NRFI estimates responsibly for bankroll and staking decisions

Translate probability estimates into stake sizing principles conservatively. Because model error and sample limitations can be large for a single inning event, many experienced players prefer small, disciplined stakes rather than aggressive sizing. Record each play and the inputs you used so you can measure performance over a sufficient sample before escalating exposure. See how our evaluations work.

Reduce exposure when uncertainty is elevated, for example when the starter has limited first inning appearances, when lineups are late and incomplete, or when weather forecasts are unsettled. Keep a log of bets with your predicted probability and the market odds to evaluate whether the model shows positive edge over time.

Quick pre-play NRFI checklist

1) Verify league baseline S from TeamRankings. 2) Pull both teams' first inning for and against rates. 3) Check starting pitcher's first inning splits on FanGraphs. 4) Confirm park factor from Baseball Savant. 5) Check game day temperature and wind. If any input is missing or uncertain, pause and document the gap.

Use fast sources for last-minute changes: TeamRankings for quick league and team tables, FanGraphs leaderboards for starter splits, and Baseball Savant park pages for venue context. See our blog. Log the chosen probability, odds taken, and the rationale behind any nonstandard adjustments for later review.

Conclusion: realistic expectations and next steps

The central takeaway is straightforward: NRFI probability starts from 1 minus the first inning scored percentage and then improves in accuracy when you layer team-level splits, starting pitcher first inning data, park factors, and conservative weather adjustments. Keep inputs current and document every decision so your process is auditable.

For ongoing refinement, revisit TeamRankings for updated first inning tables, consult FanGraphs for fresh pitcher splits, and use Baseball Savant for park factor updates as the season progresses. Treat small samples and sudden role changes cautiously and prioritize consistent record keeping over chasing isolated results. Visit Funded Plays.

What exactly is an NRFI bet?

An NRFI bet wins if no runs are scored in the top or bottom of the first inning; it is a single event that settles on the first inning box score.

Where can I get the league baseline for NRFI?

Use a continuously updated first inning scored percentage table such as TeamRankings to compute the league NRFI baseline as 1 minus the scored percentage.

How much does weather change NRFI odds?

Weather, especially temperature and wind, can modestly change first inning scoring risk; treat any adjustment conservatively because effects are real but often small.

Use the identity between first inning scoring and NRFI as the foundation for every estimate, and refresh key inputs regularly. Keep records, apply conservative adjustments for small samples and weather, and treat any betting decision as conditional on the documented inputs.