Another trade deadline has passed. As you probably already know, the D-backs shipped off a few players, including Gerardo Parra, Martin Prado, and Brandon McCarthy. Throughout the Kevin Towers era, there have been mixed results on the trade front. There have been good trades: Towers acquired Aaron Hill and Brad Ziegler in two separate, successful deals. But more recently the trades have been more bad than good. Since the trades have been unfavorable, I wondered if Towers was doing something wrong strategically. Specifically, the circumstances surrounding the Justin Upton trade stood out. In case you don’t remember, the D-backs had bashed Upton publicly to the point where the basically had to trade him. They backed themselves into a corner. Using the Justin Upton scenario as an example, we’re going to explore whether necessitating a certain player be traded lowers the potential return in a trade.
First, let’s set out the parameter for our example. Generally, the first team (we’ll call them “team A”) wants to trade a certain player (“player A”). This fact becomes known one way or another. Through the rumor mill, as a result of trade negotiations, whatever. So we’ll assume the teams that comprise Major League Baseball know that player A is available.
Not every team is going to be willing to trade for player A. The player needs to meet a few specifications. Potential trade partners should have a need at the position. Teams also need to be able to afford the player. Lastly, teams need to have the assets to acquire the player. There may be other team-specific factors too. The point is, the market for a given player is a subset of all teams in MLB. The teams that meet the specifications bid on the player and whoever offers the most value will complete the deal.
In order to forecast how public denouncement of a player affects trade negotiations, we need to estimate what it does. In reality, it puts the team in a corner. It informs other teams that they are unwilling to hold on to a player. Let’s jump into an example to show exactly how this will change outcomes.
Assume team A is putting player A on the trading block, and somewhere between two and thirty teams are interested. Let’s engage an example. Two teams (“team B” and “team C”) engage in a bidding war. Team B bids $100. Then team C has the option of offering more than $100 or not offering anything. It’s simple – if team C values player A at more than $100 then they’ll make an offer. If team C does make an offer, then team B is in same scenario. The teams continue to compete against each other. They drive up the price until everyone is out except one potential buyer. Using the example above, let’s say team C values player A at $90. They wouldn’t bid anymore. So team A is left with keeping player A or taking $100 for him. This is where problems arise.
Regardless of how many teams are involved in the bidding, eventually it comes down to one buyer and one seller. I’m going to use a simple line graph to illustrate the situation.
There are two possibilities. One is that the team B’s bid is greater than team A’s value for player A. In that case, a deal is made and there’s no problem. The other possibility is that Team B’s last bid falls short of Team A’s value for player A, as the graph above (poorly) illustrates. Normally, in this situation Team A can reject the offer. Then team B can increase their offer until they reach either team A’s value or their own value for player A. If team B’s value for player A is greater than team A’s value, then a deal will be made. If not, no deal. Either way, team A will not be worse off than before.
This whole scenario collapses when team A is forced to trade player A. Once one team outbids the others, it has no incentive to continue to raise its bid in order to satisfy team A. Team A cannot back out; they have to take the best they can get.
This is a long-winded, game theory-inspired way of saying that tipping your hand causes you to lose leverage in negotiations. In certain situations it can really hurt a seller. Giving away free information serves no purpose. It can be incredibly costly, and there’s no benefit.
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Previously on The Pool Shot, the guys explained some of their favorite advanced stats. Hitting, including wRC+, HHAV and batted ball; pitching (38:00), including FIP, xFIP and SIERA; and baserunning and defense, including UBR, UZR and DRS (58:00).