Item Slam Patch 7.41f
Bloodstone

Bloodstone: best heroes by WPA

Bloodstone in patch 7.41f: WPA of the purchase on 3 heroes, by role and build slot, against the other items bought in the same slot. Highest WPA, still inconclusive: Queen of Pain core, 2nd item (+2.52 pp).

By hero and slot

WPA of the item against the other items bought in the same slot, in matches of every rank. Slots with fewer than 30 purchases are left out.

Hero Role Slot Purchases WPA 95% CI Verdict
Queen of PainCore2nd item43 (0.7%)+2.52−2.49 to +7.04Inconclusive
Queen of PainCore4th+ item476 (5.5%)+2.34−0.09 to +5.10Inconclusive
Queen of PainCore3rd item87 (1.8%)+1.87−1.60 to +4.90Inconclusive
PudgeSupport4th+ item42 (0.4%)+0.91−3.60 to +5.42Inconclusive
PudgeCore3rd item67 (0.4%)+0.33−3.52 to +4.19Inconclusive
PudgeCore4th+ item389 (2.4%)−0.44−3.46 to +2.76Inconclusive
Ember SpiritCore4th+ item139 (0.7%)−3.43−8.71 to +1.15Inconclusive

How to read

What is WPA?

Win probability added: how much choosing an item changed the chance of winning, in percentage points, compared with what the hero usually buys in that same slot and situation. +1.0 means the choice won 1 percentage point more than the typical purchase. Unlike raw win rate, it accounts for the game state when the item was bought, so an item bought from ahead doesn't look good just because the game was already won.

Build slots

1st, 2nd, 3rd and 4th+ count completed items. Components that become another item in most matches (Perseverance, Dragon Lance, Kaya) do not take a slot: the item they build into does. Boots get a card of their own.

Confirmed or not

The ✓ Better or ✕ Worse badge only appears when the effect passes the false discovery control (10%) in both ways of measuring: at the start of the decision and at the moment of purchase. Without a badge, the sign of the WPA is a trend.

Where the data comes from

Ranked (All Pick) matches parsed by OpenDota in the current patch. One model estimates the chance of winning from the game state; another, the effect of each item at the time of the decision. It is association, not causation: the model sees the game, not who is playing.