grichard99

statpredict-lite

README

License: apache-2.0

Why "Lite"

"Lite" means this is an open source prototype. It is the public, fully inspectable version of the idea, released so people can read the recipe, run the model, and build on it.

The whole thing is small on purpose. A compact parameter count, a compact vocabulary, and a tokenizer built specifically for numeric structured text mean it trains on a single GPU for pennies and runs comfortably on a laptop CPU. That makes it easy to fork, retrain on your own sport or your own stat schema, and experiment with quickly.

What it does

The model is trained on a text format where the input and the target are separated by >>>:

markdown
{input json} >>> {output json}

At inference you supply everything left of the separator, and the model writes the predicted season on the right.

Example (fabricated, illustrative only):

Input:

json
{"sport":"women's basketball","sex":"female","position":"guard","position_groups":["guard"],
 "previous_seasons":[{"season":"2024-25","stats":{"G":30,"PTS":400,"AST":90,"Tot Reb":150}}]}

Output:

json
{"predicted_next_season":{"season":"2025-26","stats":{"G":31,"PTS":455,"AST":102,"Tot Reb":168}}}

Quick start

python
import json, torch
from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast

repo = "grichard99/statpredict-lite"
tok = PreTrainedTokenizerFast.from_pretrained(repo)
model = GPT2LMHeadModel.from_pretrained(repo).eval()

athlete = {
    "sport": "women's basketball",
    "sex": "female",
    "position": "guard",
    "position_groups": ["guard"],
    "previous_seasons": [
        {"season": "2024-25", "stats": {"G": 30, "PTS": 400, "AST": 90, "Tot Reb": 150}}
    ],
}

prompt = json.dumps(athlete, separators=(",", ":"), ensure_ascii=False)
ids = [tok.bos_token_id] + tok.encode(prompt, add_special_tokens=False) + [tok.sep_token_id]

with torch.no_grad():
    out = model.generate(
        torch.tensor([ids]),
        max_new_tokens=320,
        do_sample=False,
        pad_token_id=tok.pad_token_id,
        eos_token_id=tok.eos_token_id,
    )

text = tok.decode(out[0][len(ids):], skip_special_tokens=True)
print(json.loads(text))

The >>> separator is the tokenizer's sep_token. Prepend bos_token and append sep_token exactly as shown. The model was trained with that framing and will behave poorly without it.

Input schema

Table with columns: Field, Type, Notes
Field	Type	Notes
`sport`	string	One of the supported sports below
`sex`	string	`male`, `female`, or `unknown`
`position`	string	Sport appropriate position label
`position_groups`

Optional conference and division fields are also understood.

Supported sports: baseball, men's basketball, men's soccer, softball, track & field, women's basketball, women's soccer, women's volleyball.

Stat keys are sport specific and match the source records. Basketball uses PTS, AST, Tot Reb, FG% and similar; volleyball uses Kills, Digs, Aces; track & field uses Shot Put, Mile, 5000 Meters. Track & field values are in seconds for timed events and meters for field events. Unrecognized keys are not guaranteed to be handled sensibly.

How it was built

Table

Architecture	GPT-2 (`GPT2LMHeadModel`), randomly initialized
Parameters	~33M
Layers / heads / d_model	10 / 8 / 512
Context length	1024
Vocabulary	1,888 (custom BPE)
Precision	float32

The tokenizer is a custom BPE trained on the corpus itself, with digits split into individual tokens so numbers are represented consistently rather than being absorbed into arbitrary multi digit merges. Training used early stopping on a held out validation split, partitioned by athlete, so no athlete appears in both train and validation.

Full training recipe, data preparation scripts, and evaluation harness: github.com/grichard99/statpredict-lite

Training data

Trained on college athletics season stat lines compiled from public results sources.

The dataset is not published and is not included here. It is also structurally de-identified. A training record contains only sport, sex, position, position group, conference, division, season label, and numeric stat values. It contains no athlete names, no school names, and no identifiers. Those fields never existed in the training representation, so the model has no capacity to emit them. The published tokenizer vocabulary reflects this: it contains sport, position, conference, and stat name tokens only.

Every example shown in this card is fabricated.

Limitations and intended use

This is a research prototype released for inspection and experimentation. It is not a validated forecasting system, and it is not fit for any consequential decision. Please do not use it for recruiting, scouting, evaluation, wagering, or anything else affecting a real person.

No performance claims are made here. If you intend to rely on it for anything, evaluate it yourself on your own held out data first. The evaluation harness in the GitHub repo, including a naive "repeat last season" baseline to compare against, is there for exactly that purpose.

Other things worth knowing:

Coverage is limited to the eight sports listed above, and to the stat keys and season formats present in the source records.
Output is free form generated text parsed as JSON. It is usually valid, but you should wrap parsing in a try / except and handle failures.
The model tends to return the full stat schema for a sport, so it may emit stat keys you did not supply, or omit ones you did.
Athletes with unusual, sparse, or single season histories are the weakest case.
Structural zeros are common in the underlying data. A sprinter has no throwing marks, and a field player has no goalkeeping stats. This shapes model behavior in ways that are easy to misread when scoring, so score accordingly.
Greedy decoding (do_sample=False) is recommended. Sampling makes malformed output considerably more likely.

License

Apache-2.0, for both the weights and the code.

Because the model was trained from scratch with no pretrained initialization, it carries no inherited license obligations from any upstream model. The license above is the only one that applies.

Acknowledgements

Trained on a single NVIDIA RTX A4000 and evaluated on an NVIDIA A6000, both via NVIDIA Brev.

Citation

bibtex
@software{statpredict_lite,
  title  = {StatPredict Lite: a from-scratch small language model for structured
            athlete season prediction},
  author = {Richard, Guy},
  year   = {2026},
  url    = {https://huggingface.co/grichard99/statpredict-lite}
}

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