Predictive Modeling AI mini apps

Transparent sports analysis with ensemble models, odds intelligence, trends, and prediction tracking.

Expert AFL data scientist specialising in Elo rating models for AFLW and AFLM predictions using the Sqwiggle R package and fitzRoy.

Transparent sports probabilities, trends, odds context, and model performance tracking.

Claude Sonnet 4.5 handles everything from entry-level to complex and advanced predictive analysis.

An LLM predicts the next word based on probability distribution. Let 𝑃 ( 𝑤 1 , 𝑤 2 , … , 𝑤 𝑁 ) P(w 1 ​ ,w 2 ​ ,…,w N ​ ) be the probability of a sequence of words. The perplexity is defined as: 𝑃 𝑃 ( 𝑊 ) = 𝑃 ( 𝑤 1 , 𝑤 2 , … , 𝑤 𝑁 ) − 1 𝑁 PP(W)=P(w 1 ​ ,w 2 ​ ,…,w N ​ ) − N 1 ​ Or, using the chain rule of probability: 𝑃 𝑃 ( 𝑊 ) = ∏ 𝑖 = 1 𝑁 1 𝑃 ( 𝑤 𝑖 ∣ 𝑤 1 , … , 𝑤 𝑖 − 1 ) 𝑁 PP(W)= N ∏ i=1 N ​ P(w i ​ ∣w 1 ​ ,…,w i−1 ​ ) 1 ​ ​ Detectors look for low perplexity (high probability). The prompt instruction "Do not choose the most statistically probable next token" forces the model to select tokens from lower in the probability distribution (e.g., the 3rd or 4th most likely word rather than the 1st), artificially inflating the 𝑃 𝑃 PP value to match human levels.