投注分析工具 - 赔率转换、优势计算、套利检测与盘口变动分析

betting by machina-sports/sports-skills

213 周安装量

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安装命令

npx skills add https://github.com/machina-sports/sports-skills --skill betting

Python Web框架数据分析金融科技

🇨🇳中文介绍

投注分析

在编写查询之前，请查阅 references/api-reference.md 以了解赔率格式、命令参数和关键概念。

快速开始

sports-skills betting convert_odds --odds=-150 --from_format=american
sports-skills betting devig --odds=-150,+130 --format=american
sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
sports-skills betting find_arbitrage --market_probs=0.48,0.49
sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
sports-skills betting line_movement --open_odds=-140 --close_odds=-160

Python SDK:

from sports_skills import betting

betting.convert_odds(odds=-150, from_format="american")
betting.devig(odds="-150,+130", format="american")
betting.find_edge(fair_prob=0.58, market_prob=0.52)
betting.find_arbitrage(market_probs="0.48,0.49")
betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
betting.line_movement(open_odds=-140, close_odds=-160)

重要提示：在任何分析之前

重要提示：在调用任何分析命令之前，请验证：

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比较 ESPN 与 Polymarket/Kalshi

获取 ESPN 的胜负盘赔率（例如，从 nba get_scoreboard 获取）：主队：-150，客队：+130
获取相同结果的 Polymarket/Kalshi 价格（例如，主队为 0.52）
去除抽水：devig --odds=-150,+130 --format=american → 公平概率：主队 57.9%，客队 42.1%
比较：find_edge --fair_prob=0.579 --market_prob=0.52 → 优势：5.9%，期望值：11.3%
或者一步完成：evaluate_bet --book_odds=-150,+130 --market_prob=0.52

从不同来源获取每个结果的最佳价格（Polymarket 主队 0.48，Kalshi 客队 0.49）
find_arbitrage --market_probs=0.48,0.49 --labels=home,away
总隐含概率 0.97 (< 1.0) → 发现套利机会，保证投资回报率：3.09%

为每个选项去除抽水：选项 1 → 0.58，选项 2 → 0.55，选项 3 → 0.50
parlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600
返回组合公平概率、优势以及凯利分数

获取 ESPN 的开盘和收盘盘口：开盘 -140，收盘 -160
line_movement --open_odds=-140 --close_odds=-160
返回概率变化、方向以及分类（专业资金动向、大众资金推动等）

示例 1：使用 ESPN 和 Polymarket 价格检查优势用户说："湖人队的比赛有优势吗？ESPN 赔率是 -150，Polymarket 价格是 52 美分" 操作：

调用 devig(odds="-150,+130", format="american") → 主队公平概率约 58%
调用 find_edge(fair_prob=0.58, market_prob=0.52) → 优势约 6%，期望值为正
调用 kelly_criterion(fair_prob=0.58, market_prob=0.52) → 最优投注比例结果：呈现优势百分比、每美元期望值以及建议的投注金额占资金的比例

示例 2：套利机会检测用户说："我能套利吗？Polymarket 主队 48 美分，Kalshi 客队 49 美分" 操作：

调用 find_arbitrage(market_probs="0.48,0.49", labels="home,away")
检查结果中的 arbitrage_found 结果：如果存在套利：呈现分配百分比和保证的投资回报率。如果不存在：呈现超额概率并解释没有保证利润

示例 3：串关注评估用户说："这个 +600 的 3 选项串关注值得投吗？" 操作：

为每个选项去除抽水以获得公平概率（例如，0.58, 0.62, 0.55）
调用 parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600) 结果：呈现组合公平概率、优势、期望值、+EV 或 -EV 的判断以及凯利分数

示例 4：盘口变动解读用户说："盘口从 -140 移动到 -160，这意味着什么？" 操作：

调用 line_movement(open_odds=-140, close_odds=-160) 结果：呈现概率变化、方向、幅度以及分类（专业资金动向、大众资金推动等）

示例 5：为标准的让分盘去除抽水用户说："这个让分盘的真实赔率是多少？两边都是 -110" 操作：

调用 devig(odds="-110,-110", format="american") 结果：呈现每边 50% 的公平概率，抽水约 4.5%

示例 6：赔率格式转换用户说："将 -200 转换为隐含概率" 操作：

调用 convert_odds(odds=-200, from_format="american") 结果：呈现 66.7% 的隐含概率和 1.50 的十进制赔率

不存在的命令 —— 切勿调用这些命令

~~get_odds~~ —— 不存在。本模块分析赔率；它不获取赔率。使用 nba-data/nfl-data/等获取 ESPN 赔率，或使用 polymarket/kalshi 获取预测市场价格。
~~calculate_ev~~ —— 不存在。请改用 find_edge 或 evaluate_bet。
~~compare_markets~~ —— 不存在。使用 markets 技能进行跨平台比较。

如果某个命令未在 references/api-reference.md 中列出，则它不存在。

错误：调用 convert_odds 时出现 ValueError: unknown format 原因：from_format 参数不是 american、decimal 或 probability 之一解决方案：准确使用 american、decimal 或 probability 作为格式字符串

错误：find_edge 在预期为正优势时返回负期望值原因：公平概率和市场概率可能颠倒了，或者跳过了去除抽水步骤解决方案：首先对博彩公司赔率运行 devig，然后将去除抽水后的 fair_prob 传递给 find_edge

错误：即使价格看起来很低，find_arbitrage 也显示没有套利机会原因：当正确包含所有结果时，价格总和可能超过 1.0 解决方案：验证您是否使用了所有结果的正确概率；检查结果中的 total_implied

错误：凯利分数非常高（大于 0.5）原因：优势估计非常大 —— 通常是由于公平概率计算错误解决方案：使用半凯利或四分之一凯利进行保守的资金分配。通过 devig 重新验证公平概率

🇺🇸English

Betting Analysis

Before writing queries, consult references/api-reference.md for odds formats, command parameters, and key concepts.

Quick Start

sports-skills betting convert_odds --odds=-150 --from_format=american
sports-skills betting devig --odds=-150,+130 --format=american
sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
sports-skills betting find_arbitrage --market_probs=0.48,0.49
sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
sports-skills betting line_movement --open_odds=-140 --close_odds=-160

Python SDK:

from sports_skills import betting

betting.convert_odds(odds=-150, from_format="american")
betting.devig(odds="-150,+130", format="american")
betting.find_edge(fair_prob=0.58, market_prob=0.52)
betting.find_arbitrage(market_probs="0.48,0.49")
betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
betting.line_movement(open_odds=-140, close_odds=-160)

CRITICAL: Before Any Analysis

CRITICAL: Before calling any analysis command, verify:

Odds format is correctly identified (american, decimal, or probability).
ESPN odds are de-vigged with devig before computing edge vs prediction market prices.
This module computes — it does not fetch. Obtain odds from sport-specific skills or polymarket/kalshi first.

Workflows

Compare ESPN vs Polymarket/Kalshi

Get ESPN moneyline odds (e.g., from nba get_scoreboard): Home: -150, Away: +130
Get Polymarket/Kalshi price for the same outcome (e.g., home at 0.52)
De-vig: devig --odds=-150,+130 --format=american → Fair: Home 57.9%, Away 42.1%
Compare: find_edge --fair_prob=0.579 --market_prob=0.52 → Edge: 5.9%, EV: 11.3%
Or all in one step: evaluate_bet --book_odds=-150,+130 --market_prob=0.52

Arbitrage Detection

Get best price per outcome from different sources (Polymarket home at 0.48, Kalshi away at 0.49)
find_arbitrage --market_probs=0.48,0.49 --labels=home,away
Total implied 0.97 (< 1.0) → arbitrage found, guaranteed ROI: 3.09%

Parlay Evaluation

De-vig each leg: Leg 1 → 0.58, Leg 2 → 0.55, Leg 3 → 0.50
parlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600
Returns combined fair probability, edge, and Kelly fraction

Line Movement Analysis

Get ESPN open and close lines: Open -140, Close -160
line_movement --open_odds=-140 --close_odds=-160
Returns probability shift, direction, and classification (sharp_action, steam_move, etc.)

Examples

Example 1: Edge check using ESPN and Polymarket prices User says: "Is there edge on the Lakers game? ESPN has them at -150 and Polymarket has them at 52 cents" Actions:

Call devig(odds="-150,+130", format="american") → fair home probability ~58%
Call find_edge(fair_prob=0.58, market_prob=0.52) → edge ~6%, positive EV
Call kelly_criterion(fair_prob=0.58, market_prob=0.52) → optimal bet fraction Result: Present edge percentage, EV per dollar, and recommended bet size as % of bankroll

Example 2: Arbitrage opportunity detection User says: "Can I arb this? Polymarket has home at 48 cents and Kalshi has away at 49 cents" Actions:

Call find_arbitrage(market_probs="0.48,0.49", labels="home,away")
Check arbitrage_found in result Result: If arbitrage: present allocation percentages and guaranteed ROI. If not: present overround and explain no guaranteed profit

Example 3: Parlay evaluation User says: "Is this 3-leg parlay at +600 worth it?" Actions:

De-vig each leg to get fair probabilities (e.g., 0.58, 0.62, 0.55)
Call parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600) Result: Present combined fair probability, edge, EV, +EV or -EV verdict, and Kelly fraction

Example 4: Line movement interpretation User says: "The line moved from -140 to -160, what does that mean?" Actions:

Call line_movement(open_odds=-140, close_odds=-160) Result: Present probability shift, direction, magnitude, and classification (sharp action, steam move, etc.)

Example 5: De-vig a standard spread User says: "What are the true odds for this spread? Both sides are -110" Actions:

Call devig(odds="-110,-110", format="american") Result: Present each side as 50% fair probability, vig is ~4.5%

Example 6: Odds format conversion User says: "Convert -200 to implied probability" Actions:

Call convert_odds(odds=-200, from_format="american") Result: Present 66.7% implied probability and 1.50 decimal odds

Commands that DO NOT exist — never call these

~~get_odds~~ — does not exist. This module analyzes odds; it does not fetch them. Use nba-data/nfl-data/etc. for ESPN odds, or polymarket/kalshi for prediction market prices.
~~calculate_ev~~ — does not exist. Use find_edge or evaluate_bet instead.
~~compare_markets~~ — does not exist. Use the markets skill for cross-platform comparison.

If a command is not listed in references/api-reference.md, it does not exist.

Troubleshooting

Error: ValueError: unknown format when calling convert_odds Cause: The from_format parameter is not one of american, decimal, or probability Solution: Use exactly american, decimal, or probability as the format string

Error: find_edge returns negative EV when a positive edge is expected Cause: Fair probability and market probability may be reversed, or de-vigging was skipped Solution: Run devig on sportsbook odds first, then pass the de-vigged fair_prob to find_edge

Error: find_arbitrage shows no arbitrage even when prices seem low Cause: Prices may sum to more than 1.0 when all outcomes are correctly included Solution: Verify you are using the correct probabilities for all outcomes; check total_implied in the result

Error: Kelly fraction is very high (greater than 0.5) Cause: Edge estimate is very large — often from a miscalculated fair probability Solution: Use half-Kelly or quarter-Kelly for conservative sizing. Re-verify fair probability via devig

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