Chapter 40 Quiz: The Future of Sports Betting

Instructions: Answer all 25 questions. This quiz is worth 100 points. You have 60 minutes. A calculator is permitted; no notes or internet access. For multiple choice, select the single best answer.

Section 1: Multiple Choice (10 questions, 3 points each = 30 points)

Question 1. In a betting exchange, "laying" an outcome means:

(A) Placing a bet that the outcome will occur (backing)

(B) Betting against the outcome occurring, effectively acting as the bookmaker for that bet

(C) Canceling a previously placed bet

(D) Placing a limit order at a price below the current market

Question 2. The primary revenue model of a betting exchange is:

(A) Embedding margin (vigorish) in the odds

(B) Charging a commission on net winnings per market

(C) Selling customer data to sportsbooks

(D) Charging monthly subscription fees

Question 3. The "oracle problem" in decentralized betting refers to:

(A) The difficulty of predicting sports outcomes accurately

(B) The challenge of reliably bringing real-world data (like game results) onto the blockchain

(C) The high cost of running nodes on a blockchain network

(D) The legal challenge of operating without a gambling license

Question 4. Which of the following is NOT an advantage of decentralized betting platforms over traditional sportsbooks?

(A) Provable fairness through auditable smart contract code

(B) No counterparty risk because funds are held by the smart contract

(C) Higher liquidity than traditional sportsbooks due to global participation

(D) Transparency of odds and total liquidity on-chain

Question 5. Micro-betting markets typically carry higher hold percentages than standard pre-game markets because:

(A) Regulators require higher margins on micro-bets

(B) Information asymmetry, model uncertainty, and the recreational orientation of the product justify wider margins

(C) Micro-bets have fewer possible outcomes

(D) The technology required is less expensive, allowing operators to charge more

Question 6. In the context of AI-driven sportsbook operations, "reinforcement learning for line setting" means:

(A) Using neural networks to analyze player performance data

(B) Training an AI agent to learn optimal pricing strategies by interacting with bettors and maximizing expected profit

(C) Automatically generating marketing content for social media

(D) Using computer vision to track player movements during games

Question 7. "Greening up" on a betting exchange refers to:

(A) Depositing additional funds into your exchange account

(B) Backing and then laying (or vice versa) the same outcome at different odds to lock in a guaranteed profit

(C) Placing environmentally sustainable bets on renewable energy events

(D) Withdrawing profits to a separate bank account

Question 8. The largest obstacle to US betting exchange adoption is:

(A) American bettors do not understand the exchange model

(B) Limited liquidity, regulatory barriers in most states, and consumer unfamiliarity

(C) Exchanges are illegal under federal law

(D) Traditional sportsbooks offer better odds than exchanges

Question 9. A key integrity concern specific to micro-betting that does not apply to traditional game-outcome betting is:

(A) Insider trading by team executives

(B) The dramatically expanded number of potentially manipulable events and the lower corruption threshold per event

(C) Point shaving by star players

(D) Referee bias in officiating decisions

Question 10. As global sports betting regulation matures, the trend is toward:

(A) Deregulation and removal of licensing requirements

(B) Convergence around common principles including KYC/AML, responsible gambling, and advertising restrictions

(C) Each country developing completely unique regulatory frameworks

(D) Prohibition of online betting in favor of retail-only operations

Section 2: True/False (5 questions, 3 points each = 15 points)

Question 11. True or False: Betting exchanges typically offer lower effective margins than traditional sportsbooks, making them more cost-effective for serious bettors.

Question 12. True or False: NLP (Natural Language Processing) systems used by sportsbooks can detect injury announcements on social media and adjust odds within seconds, often faster than human traders.

Question 13. True or False: Decentralized betting platforms have resolved the KYC/AML compliance challenge by implementing on-chain identity verification.

Question 14. True or False: Micro-betting is projected to potentially represent 30--50% of all sports betting handle in the future.

Question 15. True or False: The arms race between bettors and sportsbook AI will eventually make profitable betting completely impossible.

Section 3: Fill in the Blank (3 questions, 4 points each = 12 points)

Question 16. On a betting exchange, the difference between the best available back price and the best available lay price is called the __________, which functions as the market's effective margin.

Question 17. A blockchain mechanism that brings real-world data (such as sports results) onto the blockchain for smart contract execution is called a __________.

Question 18. The practice of a bettor backing an outcome on a traditional sportsbook at higher odds and simultaneously laying the same outcome on an exchange at lower odds for a guaranteed profit is called __________.

Section 4: Short Answer (3 questions, 5 points each = 15 points)

Question 19. Explain why the "long-term equilibrium" of sports betting markets is unlikely to be perfectly efficient, despite the increasing sophistication of AI pricing models. Identify at least three factors that preserve market inefficiency.

Question 20. Describe the key advantages and disadvantages of a liquidity pool-based automated market maker (AMM) for sports betting, compared to a traditional order book model (as used by Betfair).

Question 21. How does the rise of micro-betting change the required skillset for a quantitative sports bettor, compared to traditional pre-game modeling?

Section 5: Code Analysis (2 questions, 6 points each = 12 points)

Question 22. Examine the following code that simulates a betting exchange matching engine:

def match_orders(back_orders, lay_orders):
    """Match back and lay orders on an exchange."""
    trades = []
    for back in back_orders:
        for lay in lay_orders:
            if back["price"] >= lay["price"]:
                trade_amount = min(back["amount"], lay["amount"])
                trades.append({
                    "price": lay["price"],
                    "amount": trade_amount,
                })
                back["amount"] -= trade_amount
                lay["amount"] -= trade_amount
    return trades

(a) Identify two problems with this matching engine implementation.

(b) Write a corrected version that properly handles price priority and removes fully filled orders.

def match_orders(back_orders, lay_orders):
    """Match back and lay orders with proper price-time priority."""
    # Sort: best back (highest price) first, best lay (lowest price) first
    backs = sorted(back_orders, key=lambda x: -x["price"])
    lays = sorted(lay_orders, key=lambda x: x["price"])

    trades = []
    b_idx = 0
    l_idx = 0

    while b_idx < len(backs) and l_idx < len(lays):
        back = backs[b_idx]
        lay = lays[l_idx]

        if back["price"] < lay["price"]:
            break  # No more matchable orders

        trade_amount = min(back["amount"], lay["amount"])
        if trade_amount > 0:
            # Match at the price of the resting order
            match_price = lay["price"]
            trades.append({"price": match_price, "amount": trade_amount})
            back["amount"] -= trade_amount
            lay["amount"] -= trade_amount

        if back["amount"] <= 0:
            b_idx += 1
        if lay["amount"] <= 0:
            l_idx += 1

    return trades

Question 23. Examine the following code that calculates the return for a liquidity pool in a decentralized betting protocol:

def pool_return(
    pool_size: float,
    total_handle: float,
    margin: float,
    actual_win_pct: float,
) -> float:
    """Calculate the return for a betting liquidity pool."""
    expected_ggr = total_handle * margin
    actual_ggr = total_handle * (1 - actual_win_pct)
    return actual_ggr / pool_size

(a) Explain why actual_ggr = total_handle * (1 - actual_win_pct) is incorrect.

(b) What does actual_win_pct mean in this context, and how should the actual GGR be calculated?

(c) Write a corrected version of the function.

def pool_return(
    pool_size: float,
    total_handle: float,
    total_payouts: float,
    gas_fees: float = 0.0,
    oracle_fees: float = 0.0,
) -> dict[str, float]:
    """Calculate the return for a betting liquidity pool.

    Args:
        pool_size: Initial capital in the pool.
        total_handle: Total amount wagered through the pool.
        total_payouts: Total amount paid out to winning bettors.
        gas_fees: Blockchain transaction costs.
        oracle_fees: Oracle resolution costs.

    Returns:
        Dictionary with GGR, net return, and return percentage.
    """
    ggr = total_handle - total_payouts
    net_return = ggr - gas_fees - oracle_fees
    return_pct = net_return / pool_size * 100

    return {
        "ggr": ggr,
        "net_return": net_return,
        "return_pct": return_pct,
    }

Section 6: Applied Problems (2 questions, 8 points each = 16 points)

Question 24. You are considering three platforms for placing a $1,000 bet on an NFL game:

Assume the true probability of Team A winning is 52%.

(a) (2 points) Calculate the expected value of the $1,000 bet on each platform.

(b) (2 points) Calculate the expected cost (negative EV) per bet on each platform.

(c) (2 points) If you place 200 such bets per year, what is the expected annual impact of platform choice?

(d) (2 points) Beyond pure expected value, identify one advantage and one disadvantage of each platform.

Question 25. An AI-powered sportsbook is deploying the following technologies. For each, explain (a) how it works, and (b) the strategic implication for bettors:

(i) (2 points) NLP monitoring of social media for injury information

(ii) (2 points) Reinforcement learning for dynamic line adjustment

(iii) (2 points) Behavioral segmentation model that classifies bettors in real time

(iv) (2 points) Computer vision analysis of pre-game warmups via stadium cameras

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