How to build an NFL conditions analytics tool

Everyone believes weather moves NFL totals and almost nobody measures it, because the measurement needs two things joined: the conditions a game was played in, and the score it produced. Conditions live in weather APIs keyed by latitude and time; scores live in sports APIs keyed by game. Joining them is most of the work, and the join is where the errors get in.

• A weather API gives you a forecast for a stadium, not a result for a game.
• Roof state is the largest and cleanest conditions effect, and it is the one most often left out.
• A pooled multi-season average can hide a trend that reverses the practical answer.

One endpoint, one grouping. The entire analysis is a single call per season plus an average — the difficulty is in the bucketing, not the code.

1. 01

   Pull a season with roof state attached

   Every game row carries stadium, roof and surface alongside the scores, so there is no second call and no geocoding. Rest days come along too, which is the other conditions-adjacent variable worth having.

   One season of gamesbash

   curl -s -H "x-api-key: $BBS_API_KEY" \
     "https://api.bigballsdata.com/v1/nfl/games?season=2025"

   Real response, trimmed

   jsonjson

   {
     "data": [
       {
         "game_id": "2025_01_DAL_PHI",
         "season": 2025,
         "week": 1,
         "game_date": "2025-09-04",
         "game_type": "REG",
         "home_team": "PHI",
         "away_team": "DAL",
         "home_score": 24,
         "away_score": 20,
         "stadium": "Lincoln Financial Field",
         "roof": "outdoors",
         "surface": "grass",
         "home_rest": 7,
         "away_rest": 7
       }
     ]
   }

2. 02

   Bucket the roof values — this is the step that decides your answer

   There are four values and only two buckets. `dome` and `closed` are both roof-shut; `outdoors` and `open` are both roof-open, `open` being a retractable stadium with the roof back. Putting `open` with `dome` because both sound like stadiums with roofs is the mistake that changes the number.

   The bucketingts

   type Roof = 'dome' | 'closed' | 'outdoors' | 'open';
   
   // closed  = retractable roof SHUT      -> plays like a dome
   // open    = retractable roof BACK      -> plays like outdoors
   const indoor = (roof: Roof) => roof === 'dome' || roof === 'closed';

3. 03

   Split the scoring, then look at it per season

   The pooled gap is +3.87 over six seasons. Print it per season as well: the trend is the finding, and a single pooled number would have hidden it.

   conditions.tsts

   const KEY = process.env.BBS_API_KEY!;
   const api = async (p: string) =>
     (await fetch(`https://api.bigballsdata.com${p}`, { headers: { 'x-api-key': KEY } })).json();
   
   type Game = { season: number; roof: string | null; home_score: number | null; away_score: number | null };
   const indoor = (r: string | null) => r === 'dome' || r === 'closed';
   
   const seasons = [2020, 2021, 2022, 2023, 2024, 2025];
   const all: Game[] = (
     await Promise.all(seasons.map((y) => api(`/v1/nfl/games?season=${y}`)))
   ).flatMap((r) => r.data as Game[]);
   
   // A null score is an unplayed game; a null roof is a stadium we have no value
   // for. Dropping them is not the same as counting them as zero.
   const played = all.filter((g) => g.home_score !== null && g.away_score !== null && g.roof !== null);
   const total = (g: Game) => g.home_score! + g.away_score!;
   const mean = (xs: number[]) => xs.reduce((a, b) => a + b, 0) / xs.length;
   
   for (const y of seasons) {
     const s = played.filter((g) => g.season === y);
     const i = s.filter((g) => indoor(g.roof)).map(total);
     const o = s.filter((g) => !indoor(g.roof)).map(total);
     console.log(`${y}  indoor ${i.length.toString().padStart(3)} @ ${mean(i).toFixed(1)}   outdoor ${o.length.toString().padStart(4)} @ ${mean(o).toFixed(1)}   gap ${(mean(i) - mean(o)).toFixed(2)}`);
   }
   
   const i = played.filter((g) => indoor(g.roof)).map(total);
   const o = played.filter((g) => !indoor(g.roof)).map(total);
   console.log(`\nPOOLED  indoor ${i.length} @ ${mean(i).toFixed(2)}   outdoor ${o.length} @ ${mean(o).toFixed(2)}   gap ${(mean(i) - mean(o)).toFixed(2)}`);

   Real response, trimmed

   jsonjson

   2020  indoor  93 @ 53.5   outdoor  176 @ 47.4   gap 6.12
   2021  indoor  83 @ 48.7   outdoor  202 @ 45.0   gap 3.76
   2022  indoor  86 @ 47.7   outdoor  198 @ 42.5   gap 5.21
   2023  indoor  86 @ 46.6   outdoor  199 @ 42.6   gap 4.05
   2024  indoor  98 @ 47.4   outdoor  187 @ 45.3   gap 2.18
   2025  indoor  92 @ 46.9   outdoor  193 @ 45.5   gap 1.39
   
   POOLED  indoor 538 @ 48.50   outdoor 1155 @ 44.63   gap 3.87

Six seasons of games is a small number of calls, so this build is cheap either way. The paid tier is about the archive rather than the volume: a one-season split has an n of roughly 285 and a confidence interval wide enough to swallow the effect.

The most recent completed season, 1,000 requests a day.

All six seasons — the 1,693-game sample the pooled figure is computed on.

• 2025 alone gives a +1.39 gap on 285 games. Six seasons give +3.87 on 1,693.
• The per-season trend is only visible with the archive; one season is a point, not a direction.
• Six calls, one per season, and the result caches forever because completed games do not change.

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Is +3.87 points a real effect or a small-sample artefact?

It is computed on 1,693 completed games, 538 indoor and 1,155 outdoor, which is a large sample for a league that plays about 285 games a season. The more useful caveat is not sample size but drift: the per-season gap runs 6.12, 3.76, 5.21, 4.05, 2.18, 1.39, so the pooled figure averages a declining effect. If you are modelling next week, 2025’s +1.39 is the more relevant number.

Why does the bucketing matter so much?

Because two of the four roof values are retractable stadiums in opposite states. `closed` is a roof-shut game and behaves like a dome; `open` is the same stadium with the roof back and behaves like outdoors. Grouping by "has a roof" rather than "was covered" moves games between buckets and changes the answer. The endpoint gives you the state, not the stadium type, which is the useful thing.

Can I get wind speed or temperature for a game?

No. That is the honest answer rather than a partial one: the conditions table is empty and the fields are held pending a weather licence. There is a forecast endpoint for other sports, but it needs a geocoded venue and NFL matches do not carry one.

Does this cover college football?

Not through this endpoint. /v1/nfl/games is NFL only. NCAAF games are in the unified /v1/matches surface, which does not carry roof.