Showstone.io provides data-driven predictions for NBA player props by combining historical statistics, matchup evaluation, contextual factors, and performance trends. The goal is to help users quickly understand how a player’s upcoming game compares to their past performance and typical game environments.
This document explains:
We describe what we consider, not the precise internal math used to generate individual outputs.
Showstone’s predictions are built on top of publicly available basketball data. We organize this information into several core categories to capture performance, context, and matchup effects.
We track each player’s historical game logs, including:
This helps us understand how a player typically performs in different situations and how consistent they have been over time.
Not all opponents are equal. We evaluate how each team performs defensively against different positions and stat types, taking into account:
Game context has a direct impact on stat lines. Some of the factors we consider include:
We look at both recent form and longer-term trends to avoid overreacting to one hot or cold stretch. This includes:
From the raw data above, Showstone computes several high-level metrics that make it easier to interpret an upcoming spot. The exact formulas and internal weights for these metrics are proprietary and not disclosed.
Trend indicators summarize how a player’s recent performance compares with their typical baseline over a longer period. They help highlight whether a player appears to be trending up, trending down, or performing in line with expectations.
Matchup difficulty ratings estimate how tough or favorable an opponent is for a given stat category. These ratings account for:
Opportunity indicators reflect how likely a player is to have chances to accumulate stats in a given game context, incorporating ideas such as:
Game context adjustments capture how environmental factors may push expected outcomes up or down, including pace, likely competitiveness, and situational elements that could impact playing time.
Internally, Showstone uses a combination of statistical modeling and data-driven rules to turn all of the inputs and derived metrics into projected stat outcomes, confidence ratings, and edge estimates.
To protect our competitive edge, we do not disclose:
What appears on the Showstone interface are simplified, interpretable outputs designed for end users: projected values, matchup difficulty, confidence ratings, and edge estimates.
Showstone surfaces several key metrics to help users interpret an upcoming scenario quickly.
The projected value is an estimate of how a player is expected to perform in a specific stat category (for example, points, rebounds, or assists), given their historical performance, matchup, and context.
The matchup difficulty rating summarizes how challenging the upcoming opponent is for that particular stat. A more difficult matchup means the opponent typically does a better job of limiting that type of production.
The trend signal highlights whether a player appears to be performing above, below, or roughly in line with their usual baseline. This can reflect increased usage, changing roles, or a streak of unusually strong or weak performances.
Context adjustments capture factors like pace, rest, venue, and rotational changes that may influence how likely a player is to reach or exceed typical stat levels in a given game.
The Confidence Rating reflects how strongly an upcoming projection is supported by historical trends, matchup data, and contextual stability.
A higher confidence rating generally means:
A lower confidence rating can indicate:
The confidence rating is meant to summarize how dependable a projection appears based on observed data. The underlying calculations that produce this rating are proprietary and are not disclosed. It should be used as a guide, not as a guarantee of any outcome.
The Edge metric measures how the data-driven view of a player’s performance compares with what is implied by market odds. At a high level, edge looks at the difference between:
For example, if a particular line at a certain price implies that the player would need to succeed about 53.5% of the time to break even,
and the historical/contextual data suggests something closer to 58%, then the edge would be the difference:
Edge = 58% − 53.5% = +4.5%
A positive edge means the data suggests a higher success rate than what is implied by the listed odds. A negative edge means the opposite.
Important notes:
Internally, Showstone monitors how well its projections align with actual game outcomes over time. This includes tracking:
These internal evaluations help improve the system over time, but detailed accuracy formulas, thresholds, and validation procedures remain private and are not published.
All projection systems have limitations. Even when built on strong data, real-world outcomes can diverge significantly from expectations. Some of the factors that can impact results include:
Showstone’s predictions should be viewed as informational tools that summarize trends and context, not as guarantees or promises of any specific outcome.
Showstone.io combines historical performance, matchup difficulty, contextual factors, and derived metrics to create interpretable NBA player prop predictions. Users can see projected values, matchup difficulty ratings, trend signals, confidence ratings, and edge metrics that summarize how a given spot compares to historical patterns.
The underlying models, exact formulas, and internal scoring logic are intentionally kept private. This balance allows us to share useful, transparent information while preserving the proprietary methods that make Showstone unique.