A franchise valuing a player does not read the published career figures. It reads a reconciled dataset built by analysts who have already found what is wrong with the public version.
Public records are assembled from many sources
A player's career spans domestic competitions, franchise leagues and international cricket, each with its own scoring operation and its own level of detail.
Ball-by-ball data exists for well-covered competitions and not for others. A career total can combine richly recorded matches with entries that are only a line in a scorecard.
Aggregating across those without adjustment produces a number that looks uniform and is not. Analysts split the record by data quality before doing anything else.
Identity resolution is the first job
The same player appears under different name spellings across competitions, and different players share names. Matching records to a single identity is a substantial and error-prone task.
Errors here are expensive because they merge or split careers. A player credited with another's performances is a valuation error nobody would spot from the totals.
Commercial data providers maintain identity mappings for this reason, and franchises generally buy that mapping rather than rebuild it.
Context has to be reattached
A strike rate means little without the phase it was scored in, the ground, the opposition strength and the match situation. All of that is available only where ball-by-ball data exists.
Analysts therefore weight matches by how much context is recoverable. Performances in well-instrumented competitions carry more weight than equally impressive ones with thin records.
This produces a systematic bias toward players who happened to play in well-covered competitions, which is a known limitation rather than a solved problem.
Missing data is not neutral
Records are typically thinner for lower-profile competitions, which are also where many uncapped players build their case.
The players hardest to evaluate are therefore precisely those whose valuation is most uncertain, and uncertainty translates into cautious bidding.
Franchises that invest in scouting those competitions directly are buying information the data market has not produced, which is where the genuine edge sits.
Why the audit is repeated every cycle
Data providers correct historical records continually, so a dataset built last cycle differs from one built this cycle even for matches long completed.
Reconciling to a fixed snapshot is standard practice, so that a valuation can be explained afterward against the data it was actually made from.
Without that discipline, a decision cannot be reviewed. The snapshot is a record-keeping practice imported into a purchasing process.

