This article explains how to assess whether Aaron Judge can break the modern American League single-season home run record. It focuses on a reproducible, park- and schedule-aware pace-tracker method that converts observed rates and playing-time assumptions into an estimated probability range. Readers will get a clear statement of the 62-homer benchmark, an outline of the inputs you need to track, and a hands-on example you can run in a spreadsheet or simple script. The goal is transparency: show how assumptions change the odds instead of asserting a single definitive forecast.

Judge set the AL single-season benchmark at 62 homers; any chase must be evaluated with park and playing-time context.

A park- and schedule-aware pace tracker translates in-season performance into a probability distribution that updates as new data arrive.

Small changes in playing time or per-PA HR rate can materially shift the odds of breaking 62 homers.

Short answer and what this article will do

Quick verdict

Aaron Judge set the modern American League single-season home run benchmark at 62, and any assessment of whether he can surpass that mark must start from that number and then layer park, schedule, playing time, and health assumptions into a transparent tracker MLB.com coverage of Judge's 62 homers.

How we will estimate aaron judge home run odds

This piece uses a park-factor normalization and pace-tracker approach so readers can follow the same steps in a spreadsheet or simple script; the method relies on standard park-factor concepts that normalize to a league-average index of 100, which lets us compare stadium environments and convert raw counts into expectation-adjusted rates FanGraphs park-factor primer.

pace-tracker template for estimating expected HR from per-PA rates and PAs

Use home and road splits externally and update weekly

What counts as the record: definition and context

The 62-HR benchmark and why it matters

The modern AL single-season record to beat is 62 home runs, set by Aaron Judge in 2022; that is the concrete target any chase must top before considering seasonal adjustments MLB.com coverage of Judge's 62 homers.

When evaluating a potential record run, the distinction between raw home run totals and context-adjusted expectation matters: raw counts are what the record uses, but to estimate odds we translate observed and projected performance into park-adjusted opportunity so different seasons and stadiums compare fairly FanGraphs park-factor primer.

Since MLB adopted the balanced schedule in 2023, each team faces a wider cross-section of opponents and parks over 162 games, which subtly changes the distribution of pitcher quality and ballpark environments compared with older, division-heavy slates MLB explanation of the balanced schedule.

Park factors let us adjust a player's raw home and road rates to a league-indexed expectation; normalizing to 100 removes the home stadium advantage or penalty so we can estimate how many homers the same rate would produce in an average environment FanGraphs park-factor primer.

Core framework: how to translate season performance into odds

Inputs and assumptions

The core inputs are per-plate-appearance home run rates, a park-factor split for home and road, a projected plate-appearance (PA) total based on lineup slot and health, an opponent adjustment that reflects the balanced schedule, and an explicit injury or missed-games probability; combining these yields an expected HR total and a variance model for simulation, similar in spirit to how projection systems blend aging, park, and playing-time risks FanGraphs ZiPS projection overview. FanGraphs projections

Explore the FundedPlays Challenges page for structured practice at disciplined forecasting

Follow the step-by-step tracker and import the same column layout described below to test alternate PA and park-factor assumptions in your spreadsheet.

Visit FundedPlays Challenges

Start by converting a recent season per-PA HR rate into separate home and road expected rates by applying the home and road park-factor indices, then multiply each by projected home and road PAs to get an expected home, expected road, and combined expected season total. That combined expectation is the center point for probabilistic simulation. See the Funded Plays blog for spreadsheet layouts: Funded Plays blog

From pace to probability: simulation basics

A practical simulation approach uses three uncertain components: variation in the per-PA HR rate, randomness in per-game PA totals, and the risk of missed games. Treat the per-PA HR rate as drawn from a beta or normal distribution centered on the estimated rate with a variance matching short-term empirical dispersion, then draw PA totals per game or for the season to produce an HR count. Repeating this process creates a distribution of season totals whose tail probability above 62 defines the chance to break the record. For context, standard park-factor normalization is the baseline method used to put the per-PA numbers on a comparable scale FanGraphs park-factor primer.

Close up of a batter mid swing with stat overlay showing per PA home run rate and park factor index in minimal Funded Plays palette aaron judge home run odds

One of the strongest levers is how friendly Yankee Stadium is to right-handed power, because a comfortable home park can lift a right-handed slugger's season total via more favorable outcomes per PA; recent Statcast park-factor leaderboards have shown Yankee Stadium above league average for home runs, which materially supports a favorable home environment for a record chase Statcast park factors.

When building projections, use separate home and road per-PA adjustments rather than a single season-level modifier; that preserves the differential benefit from a hitter-friendly home park while letting the balanced schedule control road exposure.

Projected playing time and injury risk

Playing time is the second major determinant: cumulative records are highly sensitive to games and PAs missed, and sabermetric aging research shows that power tends to decline gradually into the mid-30s, increasing variance and downside risk for a 34-year-old slugger in 2026 FanGraphs aging-curve summary.

Explicitly model missed-game scenarios rather than assuming a fixed PA total; even modest probabilities of multi-week absences compress the right tail of the HR distribution and reduce the chance to exceed a high benchmark like 62.

The balanced schedule changes how often a player sees pitchers from traditionally softer or harder run environments, so opponent mix affects opportunity; account for it either by weighting per-PA HR rates across opponent subsets or by adjusting park- and pitcher-specific expected outcomes in the simulation MLB explanation of the balanced schedule.

Projection systems incorporate opponent and aging inputs in their baselines, so comparing your tracker outputs to a projection like ZiPS is useful to sanity-check assumptions about playing time and rate regression FanGraphs ZiPS projection overview.

A frequent error is extrapolating a short hot streak into a full-season total without explicitly modelling missed games; small-sample hot streaks inflate naive pace estimates and fail to account for the higher injury volatility that comes with aging for elite players FanGraphs aging-curve summary.

Minimalist 2D vector spreadsheet mockup visualizing aaron judge home run odds with per PA HR rate bars park factor chips and simulated probability rings on Funded Plays dark palette

Do this instead: treat early-season per-PA rates as noisy and use a Bayesian or shrinkage approach to pull extreme short-term rates toward a plausible baseline before multiplying by projected PAs.

Another common mistake is confusing raw home-run counts with normalized park-adjusted rates; using raw counts will overstate opportunity if the home park is unusually friendly, while misapplied normalization can also understate the true local benefit, so apply park factors to per-PA rates and keep the 100-index convention to maintain comparability FanGraphs park-factor primer.

Check that your park-factor source defines the index and direction the same way you do, and separate home and road adjustments rather than folding them into a single scalar whenever possible.

Early-season paces are dominated by variance; relying on a five- or ten-game HR burst to project a full season will typically overstate upside because regression to a long-term mean is likely, especially when opponent mix and home/road scheduling are not constant.

Instead, update the tracker weekly and emphasize confidence bands from simulations rather than point forecasts; the distribution carries more useful information for assessing tail outcomes like breaking 62.

Practical example: build a park- and schedule-aware pace tracker

Step 1: collect per-PA HR rate and park factors

Start with a recent per-PA HR rate calculated as total home runs divided by total plate appearances over a relevant window, then fetch separate home and road park-factor indices and convert them to multiplicative adjustments around the league index of 100; Statcast park-factor outputs are a common, up-to-date source for this purpose Statcast park factors.

Does Aaron Judge have a realistic chance to break the single-season home run record of 62?

Yes, Judge is among the few players with a conceptual chance, but the probability hinges on sustained playing time, a favorable Yankee Stadium effect, and sustained above-projection per-PA power; use a park- and schedule-aware pace-tracker to update the odds.

Step 2: project playing time and convert to expected HR

Choose a PA projection based on lineup slot and team usage, then multiply home PAs by the adjusted home per-PA rate and road PAs by the adjusted road per-PA rate to get an expected season total; this is the deterministic center that the simulation will vary around.

If you prefer a defensible baseline, compare your PA projection against preseason projection systems that blend aging and role expectations and adjust for differences; ZiPS-style baselines are a helpful sanity check for playing-time assumptions FanGraphs ZiPS projection overview. For broader projection lists see ESPN projections ESPN projections

Step 3: run simple simulations and interpret probability

Model per-PA HR rate uncertainty with a modest variance term, draw seasonal PA totals under your playing-time uncertainty, and include a discrete missed-games probability that reduces total PAs in some draws; run at least several thousand trials and record the share where simulated HR totals exceed 62 to obtain the estimated chance.

Update the tracker weekly by replacing observed PAs and HRs and re-centering the per-PA distribution; focus on how the right tail of the distribution evolves rather than single-point updates to better understand the changing odds of a record run FanGraphs park-factor primer.

Scenario walk-throughs: optimistic, baseline, and pessimistic chases

Optimistic: high PA, hot HR rate, favorable park split

An optimistic scenario assumes near-full health, a sustained elevated per-PA HR rate, and a favorable home park split; using a positive home adjustment that reflects recent Yankee Stadium results increases expected home runs, and in the optimistic tail those combined inputs push the simulated distribution into the upper chance band Statcast park factors.

Funded Plays Challenges

Visit FundedPlays Challenges

Comparing your tracker against a projection baseline can show whether your optimistic case is a plausible extended outcome or an unlikely hot-streak extrapolation; use the projection as a reference, not a guarantee. For similar analysis see Funded Plays.

Baseline: healthy but regression to projection

The baseline aligns with preseason systems that blend aging and park adjustment, which typically predict strong but not extreme power for elite hitters when healthy; this baseline offers a measured probability that accounts for expected regression and the balanced schedule's dilution of extreme home-park advantage FanGraphs ZiPS projection overview.

Under a baseline, Judge remains among the top power hitters but needs a meaningful stretch of above-projection rate and consistent playing time to reach or exceed 62.

Pessimistic: missed games or slide in power

The pessimistic case emphasizes missed games and the natural mid-30s variance in power output; modeling multi-week absences or a drifting per-PA rate downward substantially compresses the right tail and makes a 62-homer season far less likely, reflecting general aging-curve patterns FanGraphs aging-curve summary.

Because the record sits high on the tail of plausible outcomes, even modest downward shifts in playing time or per-PA rate reduce the estimated odds materially.

Bottom line: Aaron Judge can plausibly be one of the few everyday players with a meaningful conceptual chance to challenge 62 homers, but the actual odds depend heavily on playing time, the Yankee Stadium home effect, and the natural variance of a mid-30s power profile; use the park- and schedule-aware pace-tracker to convert new information into an updated probability rather than relying on headlines MLB.com coverage of Judge's 62 homers.

Key metrics and dates to track during a chase

Monitor per-PA HR rate, PA pace, weekly updates to the Yankee Stadium park-factor, opponent splits from the balanced schedule, and any injury or usage reports; these inputs let you update your simulation and see whether the right tail is expanding or contracting. For methodology details see how Funded Plays evaluations work.

Revisiting the projection baseline periodically will also help you avoid overreacting to short-term streaks and keep probability updates grounded in defensible playing-time and aging assumptions FanGraphs ZiPS projection overview.

How is the 62-home-run benchmark defined?

The benchmark is Aaron Judge's 62 home runs in 2022; it is the modern American League single-season record and the target any challenger must exceed.

Which inputs matter most for estimating odds?

Per-plate-appearance HR rates, Yankee Stadium park-factor adjustments, projected plate appearances, opponent mix, and explicit missed-games probabilities are the primary inputs.

Can early-season hot streaks reliably predict a record run?

No; short-term paces are noisy and typically regress, so use shrinkage and simulations rather than naive extrapolation.

Use the described tracker to update odds as the season unfolds, and prioritize monitoring per-PA HR rate, PA pace, park-factor updates, and injury reports. Treat projection baselines as reference points and stay cautious about extrapolating short-term streaks.