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From thinking-frameworks-skills
Analyzes an MLB game from a fantasy perspective using opponent SP quality, park factor, weather risk, bullpen state, and platoon narrative. Helps with daily start/sit decisions and streaming pitcher evaluation.
How this skill is triggered — by the user, by Claude, or both
Slash command
/thinking-frameworks-skills:mlb-matchup-analyzerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- [Example](#example)
Scenario: User needs to decide whether to start Junior Caminero (TB, RHB) for today's game. Game: Tampa Bay Rays @ Colorado Rockies, 2026-04-17, Coors Field.
Research pass (web search, cite every URL):
Normalization pass:
opp_sp_quality (COL SP viewed from TB hitters' side): Marquez is below-average -> low hitter-opposition. Score = 30 (50 = league-average starter; lower = easier matchup for hitters).park_hitter_factor: Coors wOBAcon 1.18 -> normalize so 1.00 = 50. Score = 74 (very hitter-friendly).park_pitcher_factor: inverse of above. Score = 26 (very unfriendly to pitchers).weather_risk: 0% precip + mild temp + supportive wind -> 8 (very low disruption risk).bullpen_state (for the home team, COL): closer rested, setup healthy -> 65. (For TB: closer gassed -> 35.)Platoon narrative (plain English, no jargon): "Caminero hits right-handed and faces a right-handed pitcher. This is a neutral handedness matchup, not a platoon advantage. However, Caminero's hard-hit rate vs RHP is above his season average, and Coors Field is the best hitter park in baseball, so the overall matchup is strongly positive."
Composed downstream: mlb-player-analyzer reads these signals and produces matchup_score = 40% opp_sp + 25% park_hitter + 25% platoon + 10% weather -> ~72 for Caminero today. That feeds daily_quality -> START.
Copy this checklist and track progress:
MLB Matchup Analysis Progress:
- [ ] Step 1: Collect game identifiers (home, away, date, first pitch time)
- [ ] Step 2: Identify both probable starting pitchers + handedness
- [ ] Step 3: Pull park factors (home park)
- [ ] Step 4: Pull weather forecast for first-pitch window
- [ ] Step 5: Assess bullpen state for both teams
- [ ] Step 6: Normalize each signal to 0-100 (50 = neutral)
- [ ] Step 7: Write platoon narrative (plain English)
- [ ] Step 8: Emit signal file and validate
Step 1: Collect game identifiers
Step 2: Identify probable starters
Pull from MLB.com probable pitchers (https://www.mlb.com/probable-pitchers). See resources/methodology.md for search procedure and fallbacks.
Step 3: Pull park factors
Use FanGraphs 2026 park factors (guts.aspx?type=pf). See resources/methodology.md for the normalization formula (raw factor -> 0-100 scale).
Step 4: Pull weather forecast
Use RotoWire weather (rotowire.com/baseball/weather-forecast.php). See resources/methodology.md for the risk formula.
Step 5: Assess bullpen state
Use RotoBaller closer depth chart + last-7-day usage reports. See resources/methodology.md.
Step 6: Normalize signals
Every signal must land on a 0-100 scale where 50 = league-average / neutral. See resources/methodology.md for each signal's formula.
opp_sp_quality computed from each side's perspectivepark_hitter_factor anchored so neutral park = 50park_pitcher_factor = 100 - park_hitter_factor (approximate inverse)weather_risk = rain_prob_pct x importance_multiplier, capped at 100bullpen_state computed per team (home + away)Step 7: Write platoon narrative
Per yahoo-mlb/CLAUDE.md rule 5: jargon-free or translated inline. For each key hitter of interest, state handedness and the SP's handedness, then describe the platoon edge or lack thereof in plain English. See resources/template.md.
Step 8: Emit and validate
Write to signals/YYYY-MM-DD-matchup.md using resources/template.md. Call mlb-signal-emitter for validation. Validate against resources/evaluators/rubric_mlb_matchup_analyzer.json. Minimum standard: average score 3.5+.
| Signal | Range | Meaning |
|---|---|---|
opp_sp_quality | 0-100 | Opposing starter's true-talent + today's matchup. 50 = league-average SP. Higher = tougher matchup for the hitters facing them. |
park_hitter_factor | 0-100 | 50 = neutral. >50 = hitter-friendly (Coors, Cincinnati). <50 = pitcher-friendly (Oracle, T-Mobile). |
park_pitcher_factor | 0-100 | 50 = neutral. >50 = pitcher-friendly. Typically ~= 100 - park_hitter_factor, with small corrections for park-specific effects (foul territory, etc.). |
weather_risk | 0-100 | 0 = dome or perfect conditions. 100 = high postponement + in-game disruption risk. |
bullpen_state | 0-100 | Per team. 50 = normal. >50 = bullpen healthy and rested. <50 = gassed / depleted / IL-depleted. |
Plus a narrative block covering platoon implications for both lineups.
50 is neutral, always. Every signal is anchored so 50 = league-average. If your formula produces a distribution that doesn't hit 50 at the median, it's wrong -- re-anchor.
Cite every URL. Every signal value must be traceable to a specific source URL in source_urls:. If a fact cannot be verified via web search, drop confidence to 0.3 or lower and flag in red team.
Park factors are context-dependent. Handedness splits matter: Yankee Stadium favors LHB; Fenway's Green Monster favors RHB pull-hitters. If a key hitter has extreme splits, use the handedness-specific park factor, not the composite.
Weather = rain probability x importance multiplier. A 30% rain chance matters more in a September pennant race than in April. See methodology for the importance multiplier.
Bullpen state is per team, not per game. Emit two values: bullpen_state_home and bullpen_state_away. Downstream consumers pick the one they need (e.g., the streaming-strategist cares about the opposing bullpen for a late-game lead).
Platoon narrative must be jargon-free. Never write "positive splits vs RHP." Write "hits right-handed pitchers better than left-handed pitchers." Translate every stat the first time it appears.
Dome overrides weather. If the home park is a dome (or the roof is confirmed closed), set weather_risk = 0 regardless of forecast, and note the roof status in the signal body.
Both SPs, both sides. The signal file should report opp_sp_quality from both perspectives: the home team's hitters face the away SP, and vice versa. Do not pick only one.
Key formulas:
park_hitter_factor = 50 + (raw_wOBAcon_factor - 1.00) * 200
(clamped to [0, 100]; neutral park wOBAcon ~ 1.00 -> 50)
park_pitcher_factor = 100 - park_hitter_factor (first-order approximation)
opp_sp_quality = 50 + (lg_avg_xFIP - SP_xFIP) * 15
(better SP = higher score = tougher on opposing hitters)
weather_risk = min(100, rain_prob_pct * importance_multiplier)
importance_multiplier: 1.0 (early season) .. 1.5 (playoff push) .. 2.0 (postseason)
bullpen_state = 50
- 15 if closer unavailable
- 10 per high-leverage arm unavailable
+ 5 if closer had 2+ days rest and full bullpen rested
(clamped [0, 100])
Key sources (see context/frameworks/data-sources.md for the full list):
Inputs required: home team, away team, date (YYYY-MM-DD), first-pitch time (optional).
Outputs produced: one signal file at signals/YYYY-MM-DD-matchup.md with frontmatter + matchup summary table + platoon narrative + source URLs.
Key resources:
npx claudepluginhub lyndonkl/claude --plugin thinking-frameworks-skillsAnalyzes a single MLB hitter or pitcher for Yahoo Fantasy Baseball 2K25, computing structured signals (daily_quality, regression_index, streamability_score, etc.) from web-sourced projections, stats, lineups, weather, and injuries.
Builds a structured opponent scouting report identifying tactical patterns, key personnel, set piece tendencies, and exploitable weaknesses for coaches preparing a game plan.