betting

Name: betting
Author: machina-sports

1.2kinstalls27forks

How to install betting?

machina-sports/sports-skills/betting

$npx -y skills add machina-sports/sports-skills --skill betting

Installs into the current project.

Summary

Betting analysis — odds conversion, de-vigging, edge detection, Kelly criterion, arbitrage detection, parlay analysis, and line movement. Pure computation, no API calls. Works with odds from any source: ESPN (American odds), Polymarket (decimal probabilities), Kalshi (integer pro

Files · 1

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SKILL.md

1	# Betting Analysis
2
3	Before writing queries, consult `references/api-reference.md` for odds formats, command parameters, and key concepts.
4
5	## Quick Start
6
7	```bash
8	sports-skills betting convert_odds --odds=-150 --from_format=american
9	sports-skills betting devig --odds=-150,+130 --format=american
10	sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
11	sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
12	sports-skills betting find_arbitrage --market_probs=0.48,0.49
13	sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
14	sports-skills betting line_movement --open_odds=-140 --close_odds=-160
15	```
16
17	Python SDK:
18	```python
19	from sports_skills import betting
20
21	betting.convert_odds(odds=-150, from_format="american")
22	betting.devig(odds="-150,+130", format="american")
23	betting.find_edge(fair_prob=0.58, market_prob=0.52)
24	betting.find_arbitrage(market_probs="0.48,0.49")
25	betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
26	betting.line_movement(open_odds=-140, close_odds=-160)
27	```
28
29	## CRITICAL: Before Any Analysis
30
31	CRITICAL: Before calling any analysis command, verify:
32	- Odds format is correctly identified (american, decimal, or probability).
33	- ESPN odds are de-vigged with `devig` before computing edge vs prediction market prices.
34	- This module computes — it does not fetch. Obtain odds from sport-specific skills or polymarket/kalshi first.
35
36	## Workflows
37
38	### Compare ESPN vs Polymarket/Kalshi
39
40	1. Get ESPN moneyline odds (e.g., from `nba get_scoreboard`): Home: `-150`, Away: `+130`
41	2. Get Polymarket/Kalshi price for the same outcome (e.g., home at `0.52`)
42	3. De-vig: `devig --odds=-150,+130 --format=american` → Fair: Home 57.9%, Away 42.1%
43	4. Compare: `find_edge --fair_prob=0.579 --market_prob=0.52` → Edge: 5.9%, EV: 11.3%
44	5. Or all in one step: `evaluate_bet --book_odds=-150,+130 --market_prob=0.52`
45
46	### Arbitrage Detection
47
48	1. Get best price per outcome from different sources (Polymarket home at 0.48, Kalshi away at 0.49)
49	2. `find_arbitrage --market_probs=0.48,0.49 --labels=home,away`
50	3. Total implied 0.97 (< 1.0) → arbitrage found, guaranteed ROI: 3.09%
51
52	### Parlay Evaluation
53
54	1. De-vig each leg: Leg 1 → 0.58, Leg 2 → 0.55, Leg 3 → 0.50
55	2. `parlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600`
56	3. Returns combined fair probability, edge, and Kelly fraction
57
58	### Line Movement Analysis
59
60	1. Get ESPN open and close lines: Open -140, Close -160
61	2. `line_movement --open_odds=-140 --close_odds=-160`
62	3. Returns probability shift, direction, and classification (sharp_action, steam_move, etc.)
63
64	## Examples
65
66	Example 1: Edge check using ESPN and Polymarket prices
67	User says: "Is there edge on the Lakers game? ESPN has them at -150 and Polymarket has them at 52 cents"
68	Actions:
69	1. Call `devig(odds="-150,+130", format="american")` → fair home probability ~58%
70	2. Call `find_edge(fair_prob=0.58, market_prob=0.52)` → edge ~6%, positive EV
71	3. Call `kelly_criterion(fair_prob=0.58, market_prob=0.52)` → optimal bet fraction
72	Result: Present edge percentage, EV per dollar, and recommended bet size as % of bankroll
73
74	Example 2: Arbitrage opportunity detection
75	User says: "Can I arb this? Polymarket has home at 48 cents and Kalshi has away at 49 cents"
76	Actions:
77	1. Call `find_arbitrage(market_probs="0.48,0.49", labels="home,away")`
78	2. Check `arbitrage_found` in result
79	Result: If arbitrage: present allocation percentages and guaranteed ROI. If not: present overround and explain no guaranteed profit
80
81	Example 3: Parlay evaluation
82	User says: "Is this 3-leg parlay at +600 worth it?"
83	Actions:
84	1. De-vig each leg to get fair probabilities (e.g., 0.58, 0.62, 0.55)
85	2. Call `parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)`
86	Result: Present combined fair probability, edge, EV, +EV or -EV verdict, and Kelly fraction
87
88	Example 4: Line movement interpretation
89	User says: "The line moved from -140 to -160, what does that mean?"
90	Actions:
91	1. Call `line_movement(open_odds=-140, close_odds=-160)`
92	Result: Present probability shift, direction, magnitude, and classification (sharp action, steam move, etc.)
93
94	Example 5: De-vig a standard spread
95	Use

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betting

1.2kinstalls27forks

View on GitHub

How to install betting?

machina-sports/sports-skills/betting

$npx -y skills add machina-sports/sports-skills --skill betting

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Betting Analysis
2
3	Before writing queries, consult `references/api-reference.md` for odds formats, command parameters, and key concepts.
4
5	## Quick Start
6
7	```bash
8	sports-skills betting convert_odds --odds=-150 --from_format=american
9	sports-skills betting devig --odds=-150,+130 --format=american
10	sports-skills betting find_edge --fair_prob=0.58 --market_prob=0.52
11	sports-skills betting evaluate_bet --book_odds=-150,+130 --market_prob=0.52
12	sports-skills betting find_arbitrage --market_probs=0.48,0.49
13	sports-skills betting parlay_analysis --legs=0.58,0.62,0.55 --parlay_odds=600
14	sports-skills betting line_movement --open_odds=-140 --close_odds=-160
15	```
16
17	Python SDK:
18	```python
19	from sports_skills import betting
20
21	betting.convert_odds(odds=-150, from_format="american")
22	betting.devig(odds="-150,+130", format="american")
23	betting.find_edge(fair_prob=0.58, market_prob=0.52)
24	betting.find_arbitrage(market_probs="0.48,0.49")
25	betting.parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)
26	betting.line_movement(open_odds=-140, close_odds=-160)
27	```
28
29	## CRITICAL: Before Any Analysis
30
31	CRITICAL: Before calling any analysis command, verify:
32	- Odds format is correctly identified (american, decimal, or probability).
33	- ESPN odds are de-vigged with `devig` before computing edge vs prediction market prices.
34	- This module computes — it does not fetch. Obtain odds from sport-specific skills or polymarket/kalshi first.
35
36	## Workflows
37
38	### Compare ESPN vs Polymarket/Kalshi
39
40	1. Get ESPN moneyline odds (e.g., from `nba get_scoreboard`): Home: `-150`, Away: `+130`
41	2. Get Polymarket/Kalshi price for the same outcome (e.g., home at `0.52`)
42	3. De-vig: `devig --odds=-150,+130 --format=american` → Fair: Home 57.9%, Away 42.1%
43	4. Compare: `find_edge --fair_prob=0.579 --market_prob=0.52` → Edge: 5.9%, EV: 11.3%
44	5. Or all in one step: `evaluate_bet --book_odds=-150,+130 --market_prob=0.52`
45
46	### Arbitrage Detection
47
48	1. Get best price per outcome from different sources (Polymarket home at 0.48, Kalshi away at 0.49)
49	2. `find_arbitrage --market_probs=0.48,0.49 --labels=home,away`
50	3. Total implied 0.97 (< 1.0) → arbitrage found, guaranteed ROI: 3.09%
51
52	### Parlay Evaluation
53
54	1. De-vig each leg: Leg 1 → 0.58, Leg 2 → 0.55, Leg 3 → 0.50
55	2. `parlay_analysis --legs=0.58,0.55,0.50 --parlay_odds=600`
56	3. Returns combined fair probability, edge, and Kelly fraction
57
58	### Line Movement Analysis
59
60	1. Get ESPN open and close lines: Open -140, Close -160
61	2. `line_movement --open_odds=-140 --close_odds=-160`
62	3. Returns probability shift, direction, and classification (sharp_action, steam_move, etc.)
63
64	## Examples
65
66	Example 1: Edge check using ESPN and Polymarket prices
67	User says: "Is there edge on the Lakers game? ESPN has them at -150 and Polymarket has them at 52 cents"
68	Actions:
69	1. Call `devig(odds="-150,+130", format="american")` → fair home probability ~58%
70	2. Call `find_edge(fair_prob=0.58, market_prob=0.52)` → edge ~6%, positive EV
71	3. Call `kelly_criterion(fair_prob=0.58, market_prob=0.52)` → optimal bet fraction
72	Result: Present edge percentage, EV per dollar, and recommended bet size as % of bankroll
73
74	Example 2: Arbitrage opportunity detection
75	User says: "Can I arb this? Polymarket has home at 48 cents and Kalshi has away at 49 cents"
76	Actions:
77	1. Call `find_arbitrage(market_probs="0.48,0.49", labels="home,away")`
78	2. Check `arbitrage_found` in result
79	Result: If arbitrage: present allocation percentages and guaranteed ROI. If not: present overround and explain no guaranteed profit
80
81	Example 3: Parlay evaluation
82	User says: "Is this 3-leg parlay at +600 worth it?"
83	Actions:
84	1. De-vig each leg to get fair probabilities (e.g., 0.58, 0.62, 0.55)
85	2. Call `parlay_analysis(legs="0.58,0.62,0.55", parlay_odds=600)`
86	Result: Present combined fair probability, edge, EV, +EV or -EV verdict, and Kelly fraction
87
88	Example 4: Line movement interpretation
89	User says: "The line moved from -140 to -160, what does that mean?"
90	Actions:
91	1. Call `line_movement(open_odds=-140, close_odds=-160)`
92	Result: Present probability shift, direction, magnitude, and classification (sharp action, steam move, etc.)
93
94	Example 5: De-vig a standard spread
95	Use

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