Sports organizations generate massive volumes of data: play-by-play narratives, scouting reports, injury histories, training logs, media content, and fan communications. Chinese LLMs like DeepSeek V4, GLM-4, and Qwen3 can process this rich, unstructured information to enhance performance analysis, accelerate scouting, optimize strategies, and engage fans, all at a fraction of Western API costs. TokenEase's unified API provides sports teams, leagues, and media organizations with cost-effective access to these powerful models.
Transform raw performance data, video observation notes, and background research into professional scouting reports.
import requests
def generate_scouting_report(player_data, performance_notes, background_info, evaluation_criteria):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a professional sports scout. Generate comprehensive scouting reports with objective analysis, strengths, weaknesses, projection, and recommendation. Use standard scouting terminology."},
{"role": "user", "content": f"Criteria: {evaluation_criteria}\nBackground: {background_info}\nNotes: {performance_notes}\nData: {player_data}\n\nGenerate scouting report."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
data = """
Player: Alex Chen, 21, Point Guard, 6'3", 185 lbs
2025-26 Season: 18.5 PPG, 6.2 APG, 4.1 RPG, 1.8 SPG, 42% 3PT, 88% FT
Advanced: PER 22.4, TS% 58.2, AST% 34.5, TOV% 12.8
"""
notes = "Elite court vision in transition. Strong pick-and-roll decision maker. Can finish with either hand. Defensive intensity inconsistent. Sometimes over-penetrates.""
background = "3 years college (State University). Team captain junior year. No injury history. Parents both played professionally in China."
criteria = "NBA draft potential: 1st round vs 2nd round vs undrafted. Floor/ceiling projection. Role player vs starter vs star potential."
report = generate_scouting_report(data, notes, background, criteria)
Analyze opponent tendencies, generate game plans, and develop in-game adjustment recommendations from play-by-play data and scouting reports.
def analyze_game_strategy(opponent_data, team_strengths, recent_games, strategic_objectives):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "You are an experienced sports strategist. Analyze opponents, identify tactical advantages, and recommend game plans with specific plays, matchups, and situational strategies."},
{"role": "user", "content": f"Objectives: {strategic_objectives}\nRecent: {recent_games}\nStrengths: {team_strengths}\nOpponent:\n{opponent_data}\n\nDevelop game strategy."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
opponent = """
Opponent: Metro City FC
Tendencies: High pressing (first 20 min), 4-3-3 formation, 62% possession avg
Strengths: Wing play, set pieces (scored 12 from corners), rapid transitions
Weaknesses: Vulnerable to counter-attacks when pressing, goalkeeper poor distribution, central defense slow on turns
Key players: #7 (RW) - 14 goals, #10 (CAM) - 9 assists, #4 (CB) - slow, prone to errors under pressure
"""
my_strengths = "Fast counter-attacking, strong central midfield, clinical finishing, aerial dominance in defense"
recent = "Won 3 of last 5, scoring 8 goals. Conceded 3 set pieces in last 2 games."
objectives = "Control game after first 25 minutes, minimize set piece exposure, exploit their defensive weaknesses"
strategy = analyze_game_strategy(opponent, my_strengths, recent, objectives)
Analyze training logs, medical histories, and workload data to predict injury risk and recommend load management strategies.
def assess_injury_risk(player_profile, training_load, medical_history, upcoming_schedule):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Assess sports injury risk based on workload, history, and biomechanical factors. Recommend load management, rest periods, and preventive measures. Flag high-risk scenarios."},
{"role": "user", "content": f"Schedule: {upcoming_schedule}\nHistory: {medical_history}\nLoad: {training_load}\nProfile: {player_profile}\n\nAssess risk and recommend."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
profile = "Sarah Martinez, 26, striker, 5'9", 140 lbs. 7 seasons professional. High intensity player."
load = "Last 4 weeks: 3,200 min played (high), 28 sprints/game avg, 11 km distance/game. Gym: 4 sessions/week. Recovery: 6 hrs sleep avg (concerning)."
history = "2023: Hamstring strain (missed 6 games). 2024: Ankle sprain (missed 3 games). 2025: Clean season. Previous: ACL reconstruction 2020 (fully recovered)."
schedule = "Next 3 weeks: 5 matches, 2 travel games (cross-country), 1 cup match (extra time risk)."
assessment = assess_injury_risk(profile, load, history, schedule)
Generate match previews, post-match summaries, player profiles, and social media content tailored to different fan segments.
def generate_fan_content(content_type, match_data, team_context, target_audience):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "kimi-k2",
"messages": [
{"role": "system", "content": "Generate engaging sports content for fans. Balance excitement with accuracy. Include key stats, storylines, and emotional hooks appropriate to the audience."},
{"role": "user", "content": f"Audience: {target_audience}\nContext: {team_context}\nData: {match_data}\nType: {content_type}\n\nGenerate content."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
match = """
Result: Eagles 3-2 Thunder (OT)
Key moments: Thunder led 2-0 at halftime. Eagles comeback with goals at 67', 78'. Thunder red card at 82'. Eagles winner at 94'
Stats: Eagles 58% possession, 18 shots (7 on target). Thunder 42%, 9 shots (4 on target)
Standings: Eagles move to 2nd place, 1 point behind leaders. Thunder drop to 5th.
"""
context = "Eagles: 5-game unbeaten streak. Thunder: Defending champions, struggling with injuries to 3 starters. Rivalry match - first meeting since last season's playoff controversy."
audience = "Die-hard fans, ages 25-45, emotionally invested in rivalry"
content = generate_fan_content("Match recap with emotional narrative", match, context, audience)
Analyze athlete interviews, press conference transcripts, and team communications for psychological insights and performance indicators.
def analyze_performance_narrative(transcript_text, athlete_profile, context_factors, analysis_focus):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Analyze sports narratives for psychological state, confidence levels, team dynamics, and potential performance indicators. Maintain professional, non-clinical perspective."},
{"role": "user", "content": f"Focus: {analysis_focus}\nContext: {context_factors}\nProfile: {athlete_profile}\nTranscript:\n{transcript_text}\n\nAnalyze."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
transcript = """
Reporter: How do you feel about the team's chances this season?
Athlete: I mean, we're working hard. Every day. But, uh, sometimes it feels like we're not really clicking, you know? Like last year we had that energy, that thing. Now it's... different. But we're professionals. We'll figure it out.
Reporter: What about the new coach's system?
Athlete: It's... it's an adjustment. For everyone. Takes time to learn new things. Especially when you've been doing something one way for so long. I'm trying to stay positive."""
profile = "Veteran player, 12 years pro, previously outspoken team leader, now playing reduced minutes"
context = "Team started 2-8 after coaching change. Locker room reportedly divided."
focus = "Leadership dynamics, confidence levels, team cohesion indicators"
analysis = analyze_performance_narrative(transcript, profile, context, focus)
Analyze player contracts, salary cap implications, and trade scenarios to support front office decision-making.
def analyze_contract_scenario(player_contract, team_salary_cap, comparable_contracts, strategic_goals):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Analyze sports contract scenarios. Assess value, cap implications, trade feasibility, and strategic fit. Consider age curves, injury history, and market comparables."},
{"role": "user", "content": f"Goals: {strategic_goals}\nComparables: {comparable_contracts}\nCap: {team_salary_cap}\nContract:\n{player_contract}\n\nAnalyze scenario."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
contract = "Player: 28-year-old All-Star center. Current: 4 years $120M remaining ($30M/yr). Player option for final year. No-trade clause."
cap = "Team cap: $175M committed. Cap space available: $15M. Luxury tax threshold: $190M. Repeat offender status."
comparables = "Similar age/skill centers: 4yr/$112M, 3yr/$85M, 5yr/$145M (younger). Market declining for traditional bigs."
goals = "Compete now while core is in prime, avoid luxury tax if possible, maintain flexibility for 2027 free agency class"
scenario = analyze_contract_scenario(contract, cap, comparables, goals)
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