A practical explanation of how hedge trading works in football betting, including data standards, odds collection, and normalization.

How Can One Hedge in Football Betting? (Part 1)

1. What kind of data format is needed?

Answer: As with other quantitative trading products in finance, both the trading process itself and the strategy research and backtesting process require data under a standardized format. Football betting data mainly includes the league, match time, the two teams, the market type, the bookmaker odds, and for exchange trading, a flag indicating whether the order is a buy or sell. Because different currencies are involved, a currency field is also required. Since odds may expire over time, the timestamp at which each odds quote was captured should also be stored so that backtests can reconstruct the correct timeline and avoid comparing odds from different time points.

In my system, I use the following standardized data format:

eventid,dealer,lay,bettype,handicap,odd_home,odd_draw,odd_away,currency,Max_amount,Min_amount,other,timestamp

This single row can distinguish different leagues and matches while recording the odds and market movement for each bettype change. I have used this format for a long time and have not encountered any major issues.

The eventid contains:

D____T____L____E____VS____,

Uppercase letters are used to separate different fields. In languages other than Chinese, a length suffix for the following field should be appended after each uppercase letter. For example:

D20260701T13:00L9world cupE6franceVS6sweden

Other implicit standards in the normalized data include: all letters except the separator letters should be lowercase, special symbols should be removed, and brackets should be removed. The date and start time timezone should also be fixed. I use East Eight Time, so I convert all match data to East Eight Time immediately after it is fetched. The start time is only precise to HH:MM.

Using standardized data has many advantages: the information is comprehensive, the format is fixed, and the other field can be expanded. For example, when using Polymarket data, each row must carry an order hash. In this standardized format, that hash can be stored as a dictionary inside the other field.

Regarding the choice of database, I have seen some people recommend MongoDB. I think this is mainly for future expansion of fields that have not yet been identified. In my data format, there are no additional fields that need to be extended, so an SQL-style database is sufficient. That said, MongoDB can also handle this task well.

2. How can odds data be obtained?

Answer: There are three ways to obtain data. The first is through professional data vendors, such as a data source provider. The second is to obtain real-time odds data through the bookmaker's live betting interface. At present, companies such as Pinnacle, Polymarket, and some legal jurisdictions that provide Betfair data APIs offer real-time odds interfaces. Different companies return data in different formats, usually as JSON with different fields and different interpretations. These need to be normalized through manual processing or AI-based recognition. The third is to obtain data through web scraping from bookmaker pages or football analysis websites. I will not give an example here. If you need help parsing specific fields, you can contact me by email and I will provide assistance.

3. How can football betting data be standardized?

Answer: Numerical data such as market prices and odds can be standardized relatively easily. However, the standardization of league and team names is the most troublesome part of cross-platform football betting trading. Different platforms often use different naming conventions for leagues and teams, especially in smaller leagues and women's leagues. I have tried several methods for name normalization. Before the rise of AI, I used fuzzy matching, machine translation matching, and local mapping dictionaries. I once extracted league and team names from 100,000 matches, deduplicated them, and built a standardization dictionary. Even after combining multiple methods, the accuracy rarely exceeded 60%. After the emergence of AI, with suitable prompts, accuracy can exceed 85%. This may be partly because I use Chinese. The normalization across other Latin-script languages may be somewhat simpler.

The process can be summarized as follows:

1. Name normalization is built on top of a complete eventid.
2. Clean the eventid derived from the raw data: convert all letters except the connector characters to lowercase, remove league-year information (such as the 2026 World Cup), season information (such as the 2025–26 Premier League season), special symbols, and replace non-English letters with their corresponding English letters. In the final eventid, the league and team information should contain only Chinese characters, lowercase English letters, digits, and spaces.
3. Translate the league, home, and away fields into the same language through a translation API (I use Chinese, though other languages are also possible).
4. Use the starttime field to construct time slots, so that different eventids are assigned to their corresponding time slots and the matching difficulty is reduced.
5. At this point, we already have groups of aliases from different data sources within the same time slot. If starttime, league (possibly), home (possibly), and away (possibly) match, then a preliminary match can be made. If any two of the three fields—league, home, and away—are exactly the same, the two names are treated as the same match.
6. Store the already organized aliases_eventid locally so it can be used for AI matching later.
7. Use the matching prompt.
8. When writing the normalized data to disk, also store the original names and establish a link between the two so that manual betting can locate the match across different bookmakers using the original name.
9. Add the names of leagues, teams, and the local mapping dictionary as the dataset grows, and later the need to call translation and AI matching will decrease.

The prompt examples are as follows:

PROMPT_SPARK = """
You are a football proper-name translator. Only translate league/home/away.
Return JSON only: index, league_zh, home_zh, away_zh, league_family,
home_candidates<=3, away_candidates<=3, confidence(high|medium|low), risk_flags.
Constraints: home/away remain unchanged; football context has priority; do not invent Chinese names; if ambiguous, return multiple candidates and mark the risk.
"""

PROMPT_SPARK_RETRY =
"""
You are a football proper-name correction translator. Return exactly one JSON object.
Requirements: preserve home/away order; prefer common Simplified Chinese names; if uncertain, reduce confidence; if English must be preserved, mark kept_english.
"""

PROMPT_PAIR =
"""
You are a football match comparison judge.
For each pair of matches, return yes/no/doubt exactly N lines and no explanation.
"""