Category Strategist — Season-Long H2H Analysis

When the user asks about their category strengths, weaknesses, or overall strategy, provide a comprehensive analysis of their performance across all 14 H2H categories.

Step 1: Gather Current Data

1. get_standings — Overall record and league position
2. get_my_roster — Current roster and season stats
3. get_scoring_categories — The 14 categories and their direction

Step 2: Load Historical Context (if memory available)

If memory tools are available:

1. get_category_trends — All recorded category assessments over time
2. get_matchup_history — Past matchup results with category breakdowns
3. get_preferences — Check for existing strategy preferences (punt categories, etc.)

Step 3: Category-by-Category Assessment

For each of the 14 categories, assess the team's strength:

Hitting Categories

Category	Direction	Assessment Method
AVG	Higher wins	Roster-wide batting average from season stats
HR	Higher wins	Total HR from roster
OPS	Higher wins	Roster-wide OPS
R	Higher wins	Total runs scored
RBI	Higher wins	Total RBI
SB	Higher wins	Total stolen bases — often carried by 1-2 specialists
B_SO	Lower wins	Total batter strikeouts — fewer is better

Pitching Categories

Category	Direction	Assessment Method
ERA	Lower wins	Roster-wide ERA — weighted by innings
WHIP	Lower wins	Roster-wide WHIP — weighted by innings
K	Higher wins	Total pitcher strikeouts
W	Higher wins	Total wins
L	Lower wins	Total losses — fewer is better
SV	Higher wins	Total saves — typically 1-2 closers
HLD	Higher wins	Total holds — middle relievers

For each category, rate as: DOMINANT / STRONG / AVERAGE / WEAK / PUNTING

Step 4: Trend Analysis (with memory)

If category trends exist in memory, analyze the trajectory:

• Which categories are improving? (WEAK → AVERAGE, AVERAGE → STRONG)
• Which are declining? (STRONG → AVERAGE)
• Which have been consistently weak? (WEAK for 3+ weeks = punt candidate)
• Which have been consistently strong? (STRONG for 3+ weeks = protect)

Cross-reference with matchup history:

• Which categories have you won most often?
• Which categories have cost you the most matchups?
• Are there categories you're losing by narrow margins? (high-value improvement targets)

Step 5: Strategic Recommendations

Category Tiers

Group the 14 categories into tiers:

Tier 1 — Dominant (protect at all costs) Categories where you consistently win. Do NOT trade away players who drive these.

Tier 2 — Competitive (push to win) Categories you win ~50% of the time. Small roster moves can flip these.

Tier 3 — Weak but Fixable (target for improvement) Categories you lose often but could improve with targeted pickups or trades.

Tier 4 — Punt Candidates Categories so weak that investing resources to fix them isn't worth it. Better to concede and dominate elsewhere.

Action Plan

Based on the tier analysis:

1. Protect — Name the categories to never sacrifice in trades
2. Target — Name 2-3 categories to improve via:
   • Free agent pickups (call get_free_agents filtered for relevant positions)
   • Trade targets (which player archetypes would help?)
3. Punt — Name categories to concede and explain the math:
   • "If you punt ERA and WHIP, you only need to win 8 of 12 remaining categories"
   • "Punting SV means you can trade your closer for hitting upgrades"
4. Category math — In a 14-category H2H matchup, you need 8 to win. Show how the strategy gets to 8+.

Trade Targets by Category Need

For each Tier 3 category, suggest the type of player to target:

• Weak in SB → "Target a speed specialist like [type of player]"
• Weak in SV → "Target a closer from a team with a surplus"
• Weak in K → "Your pitching staff needs a high-K arm"

Step 6: Save to Memory (if available)

• save_category_trend for each of the 14 categories with current assessment
• If the user makes strategic decisions (e.g., "let's punt saves"), set_preference with key punt_categories
• If specific trade targets are identified, add_to_watchlist for those player archetypes