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1. Introduction

In professional sports, the ultimate goal is winning a championship. To achieve that goal,

teams must win as many games as possible during the regular season to qualify for the playoffs.

Therefore, only in an alternative universe is there an incentive for teams to lose games during the

regular season. Yet that is exactly what happens in the National Basketball Association (NBA)

year in and year out as losing teams compete for the worst record, in what is otherwise known as

“tanking.” Tanking is defined as team management’s intention of doing as little as it can to win.

It is a concerted effort for a team to deliberately be just as bad as it can be for several months

during the season. The question begs: Why do teams engage in this tanking philosophy? The

answer is that tanking is a byproduct of a flawed NBA draft lottery system incorporated by the

NBA nearly a quarter century ago. In this system, a team can be rewarded for putting an

atrocious product on the floor every night. Therefore, this paper tests whether or not tanking is a

successful tactic NBA teams pursue, which rewards teams in the long-term for consistently

losing games in the present.

Constantly losing conveys an opportunity for general managers to select high in the

upcoming NBA Draft once the season concludes. The theory is that the higher the pick in the

NBA Draft, the better the player and the more likely that player will become a star in the league.

That discussion has accelerated over the last year: many NBA teams last offseason went into

“rebuild mode” and released or traded away many of their veteran players that could have

possibly contributed to more victories. The incentive to rebuild last offseason and potentially be

a bad team for this season likely correlates with the strength of the upcoming 2014 NBA Draft.

The 2014 class is projected as the strongest draft class in the last decade with collegiate freshmen

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phenoms such as Andrew Wiggins, Joel Embiid, and Jabari Parker expected as the top three

selections.

While team management sometimes believes their best interest is to tank and receive a

top draft pick, the NBA wishes for the opposite. League executives fear teams that eagerly

maximize losses in the present will hurt the product of the league, since fans see the act of

tanking as disgraceful. Therefore, the NBA and each team’s front office face a principal-agent

problem when teams decide tanking for a higher draft pick is beneficial. Long-term profit

motivates both sides, but the league and the team front offices each seek different approaches.

This paper explores that principal-agent relationship and possibly detects if tanking is a

successful tactic from the upper management of NBA teams.

This research paper is divided into seven sections. Section 2 provides an overview and

history of the NBA Draft lottery and also summarizes why teams tank for top draft picks.

Section 3 surveys literature regarding tanking and competitive balance. Section 4 derives an

economic theory related to the principal-agent problem and discusses if tanking in the present

benefits profit in the long-run. Section 5 introduces the methods of the empirical model, while

Section 6 analyzes the results and concludes whether the tanking strategy is efficient. Finally,

Section 7 concludes with a summary of the results and potential future research that builds off

this exercise.

2. History of the NBA Draft

Throughout most of its history, the NBA used a reverse-order draft system to determine

who receives the number one overall pick. In this system, the team that finishes with the worst

record is awarded the number one pick, the second-worst record with the second pick, etc.

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However, in order to eliminate teams losing purposefully and eradicate their incentive to obtain

the worst record, the NBA implemented an equal draft lottery system in 1985 (Soebbing, 2011).

Under this system, no teams had an incentive to tank because all non-playoff teams had an equal

chance at the number one pick. Yet in the equal draft lottery, teams close to qualifying for the

postseason can win the lottery and draft the best players. Therefore, they can further distance

themselves from the worst teams and competitive balance would decrease.

After only five years of the equal draft lottery system, NBA commissioner David Stern

again changed the system after harsh criticism and comments made around the league. Stern

employed a weighted lottery system, which provided the worst team a greater chance of winning

the NBA lottery. After the Orlando Magic won the 1993 NBA lottery, despite having the best

record of non-playoff teams and only a 1.5% chance of obtaining the number one pick, the NBA

again modified the system. NBA officials overwhelmingly voted to increase the chances of the

number one pick for the team that finished with the worst record. The team that finishes with the

worst record now has a 25% chance of obtaining the number one overall selection. Moreover,

that team can drop to no worse than the fourth selection in the lottery, which is the system that is

in place today.

However, by increasing the odds that the worst team wins the lottery, the NBA is

indirectly reintroducing the incentive to tank. Therefore, the NBA is faced with the decision of

balancing two competing goals. The NBA is set on achieving its first goal, which is to increase

competitive balance by giving the bad teams the chance to land a superstar at the top of the draft.

However, this makes it increasingly difficult for the NBA to achieve the second goal: reducing

the possibility that teams tank.

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Indeed, since the NBA introduced the weighted draft lottery system in 1990, it appears

they succeeded in increasing competitive balance and giving bad teams the opportunity of

drafting a superstar. The table below shows the percentage of players drafted from 1990-2010

selected to multiple All-NBA teams. The All-NBA team is composed of the top players in the

league, voted upon by a panel of sportswriters at the end of each season.

Draft Selection Percentage of players selected to multiple All-NBA teams

Top 5 65.85%

Picks 6-10 14.63%

Teens 9.76%

Rest of Draft 9.76%

Table 2.1

Thus, an astounding 66% of players named to at least two All-NBA teams were drafted in

the top five of the NBA Draft. This percentage does not include high school stars such as Kobe

Bryant, Amare Stoudemire, and Tracy McGrady – all likely top five selections if required to

attend college for one year due to today’s NBA minimum age rule. The full list of players

accompanied by their respective draft pick and number of All-NBA selections is displayed in the

Appendix. Therefore, while competitive balance increases, the results also establish that teams

have a strong incentive to tank due to the past success of selections in the top five of the draft.

3. Literature Review

3.1. Is tanking really a common phenomenon in the NBA?

Various authors over the last decade explore literature regarding tanking in the NBA.

Select authors believe that teams do not intend to tank during a given season. Gonzalez et al.

(2013) approach tanking from a game theory point of view. They argue that teams often get

stuck in the middle, which is the worst position for an NBA team. Gonzalez et al. (2013) claim

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that many teams pursue their dominant strategy, which is winning a championship. However,

since many other teams pursue their dominant strategy of winning an NBA title, it creates a

vicious cycle where teams spend decades straddling between the line of barely making the

playoffs or drafting late in the lottery. As long as these teams continue pursuing their dominant

strategy, they guarantee themselves that they won’t be relevant now or in the future. Instead, they

can maximize their payoffs by tanking and selecting high in the lottery as opposed to winning a

championship. Therefore, Gonzalez et al. (2013) conclude that tanking is still not a common

behavior in the NBA; teams not only pursue their dominant strategy, but they also hypothesize

that teams still place high value on the ethics of fair play.

In addition, Deeks (2014) believes that “tanking” is a mythical term that media members

substitute for “rebuilding.” He believes that some NBA teams’ optimal strategy is weakening

their current product in exchange for future flexibility and assets. Deeks (2014) identifies teams

this past season such as the Utah Jazz and Phoenix Suns, in which both teams unloaded a lot of

talent during the offseason to acquire future draft picks and younger players. Thus, this led the

media to believe that both teams tanked during the offseason. However, both teams performed

above expectations this year, and thus Deeks (2014) concludes that: 1) no team is intentionally

trying to tank and 2) youth does not guarantee poor play. Instead, it is a rebuilding process and

supposed tankers are simply better at building a young team through asset accumulation and

management. Instead, Deeks (2014) argues, “The worst team in the NBA…is the one who tries

to be good.” The weakness to his argument is that Deeks (2014) is looking at evidence from only

the 2014 season.

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3.2. Do teams intentionally tank?

While some authors believe that tanking is not a common practice, many others scholars

find evidence that NBA teams employ a tanking strategy. Taylor and Trogdon (2002) became the

first group of authors to test whether NBA teams intentionally tank. The authors investigate how

likely teams intentionally tank under the three NBA draft formats: reverse-order, equal chance

lottery, and the weighted lottery system. The results demonstrate that teams eliminated from

postseason play are 2.2 times more likely to lose under the reverse order system. When the NBA

changed the draft format to equal chance lottery, the tanking incentive diminished entirely. Yet

when the NBA implemented the weighted lottery system, tanking behavior returned as teams

eliminated from playoff contention became 2.5 times more likely to lose when playing another

team already eliminated from postseason play. This work provides clear evidence that NBA

teams did lose games to improve draft position only in seasons where losing guaranteed a better

pick. This paper refers back to Taylor and Trogdon’s (2002) piece and assumes that all losing

teams tank to improve draft position and therefore acquire a better talent.

Price et al. (2010) employ an empirical model similar to Taylor and Trogdon (2002) in

order to determine tanking behavior of NBA teams. Price et al. (2010) find that no tanking

occurred in the traditional reverse-order draft system (contrary to Taylor and Trogdon) because

in the reverse-order system, the draft order is deterministic and the marginal benefit of shirking –

an increase in one position in the draft – is constant. However, their results are mostly consistent

with Taylor and Trogdon’s (2002) primary findings, where they conclude that teams do have a

strong incentive to tank when there is a lottery system in place. In fact, Price et al. (2010) found

that tanking is a lot more consistent after the introduction of the rookie pay scale in 1995, where

teams no longer needed to pay their rookie draft picks hefty contracts. Therefore, that saved

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teams substantial amounts of money on their payroll, which resulted in higher profits at the end

of the year. Overall, by promoting competitive balance and also deterring tanking, the NBA

actually created a highly competitive secondary tournament once teams were eliminated from

postseason contention. In this secondary tournament, the losing teams compete for the number

one overall draft selection rather than a championship, which is really the ultimate goal.

Therefore, Price et al. (2010) also contribute to this paper by proving teams clearly have an

incentive to tank and acquire a high-end superstar. In addition, payroll is one of the control

variables further employed in the models in Section 6.

Walters and Williams (2012) compare games at the end of the season in which a non-

playoff team can potentially change its lottery odds by winning or losing games to teams where a

win or loss affects their lottery position. Their results show that playoff-eliminated teams are

14% more likely to lose a game when doing so directly increases their chance of acquiring a

better draft lottery position. They also find that losing teams more likely drop even more games

towards the end of the season via a tanking strategy; therefore, this paper assumes that all losing

teams in the NBA tank at some point in the season. Strictly speaking, even if a team does not

seek out tanking at the beginning of the season, they eventually tank at the end of the season so

that they can receive a better draft pick.

Research extends beyond the NBA sphere to conclude if the NBA is the sole offender of

tanking in professional sports. Borland et al. (2009) explore whether tanking exists in the

Australian Football League (AFL) that utilizes a reverse-order draft system to determine the

number one draft pick. Furthermore, the AFL also incorporates a special assistance draft, which

awards more selections to teams that do not receive a threshold of wins. Therefore, teams should

have an even stronger incentive to tank if eliminated from postseason contention. However, even

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under both a reverse-order draft system and special assistance draft, Borland et al. (2009) found

that teams showed no signs of tanking in the Australian Football League. The authors investigate

the main reason why there is no tanking behavior in Australian football: there is a difference in

roster size and inability of teams to scout and project talent in the amateur draft compared to the

NBA. Sanderson and Siegfried (2003) originally develop the argument, which the next segment

examines further.

Overall, these studies conclude that tanking does indeed occur in the National Basketball

Association. Therefore, this paper builds off that notion and infers that not only does a losing

team exploit tanking as a common behavior, but also assumes all losing teams tank at a certain

point in the season.

3.3. Why do teams tank and how does it affect competitive balance?

The upcoming literature investigates why teams engage in tanking behavior and how it

affects competitive balance. As Section 2 demonstrates, teams have a very strong incentive to

tank and select at the top of the NBA Draft to acquire a superstar. Sanderson and Siegfried

(2003) hypothesize that NBA teams tank because, “one player could constitute 20% of a starting

line-up (one out of five on the court) and where there is more agreement about a player’s

potential than in football or baseball, which have larger rosters and predictions of performance

are less reliable” (Sanderson and Siegfried, 2003). Therefore, the authors conclude that a top

draft pick should increase competitive balance in the NBA since worse teams have a high

probability of landing a superstar. Therefore, this paper contributes to Sanderson and Siegfried’s

(2003) piece by analyzing whether teams are indeed more likely to draft a better player by

selecting high in the draft.

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This contrasts with Fort and Quirk (1995), where they contend the amateur draft has no

impact on the league’s competitive balance. Instead, they establish that tanking teams strive for

profit maximization instead of win maximization. In a classic form of monopsony, drafted

players are restricted to only play for the team that drafts them under a low-salaried rookie

contract. Therefore, management purchases a player’s talents at a cheap rate and potentially

resells that talent at a higher price if that player is approaching the end of his contract. Team

executives therefore remain satisfied with profit maximization, whether they obtain a superstar

or not. The following section (Section 4) builds off this knowledge when examining the theory

behind tanking and its relationship with profit maximization.

Soebbing (2011) conducts a study and finds supportive evidence to Fort and Quirk’s

(1995) hypothesis. Soebbing (2011) conducted a difference-in-difference model using data from

the entire regular season history of the NBA (1946-2010) to measure competitive balance in the

NBA. He finds that the amateur draft is an anti-competitive practice used by management to

keep player costs down and improve profits of the owners; it is not in team management’s

interest to fulfill the NBA’s desire to use the draft as a way of increasing league-wide

competitive balance. Teams pursue their best interest, which Soebbing assumes leads to a

potential principal-agent problem between the league (the principal) and the executives (the

agents). Therefore, this paper further analyzes whether this principal-agent relationship exists

and if team management approaches the system in a different way than originally designed by

the league.

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4. Theory

In this section, this paper develops the economic theory behind tanking. Optimization

theory assumes that agents act rationally, where individuals make decisions that result in utility

maximization. At first glance, it appears that teams act irrational by tanking; rational behavior by

most NBA teams suggests teams should always maximize their wins, which should generate

greater profit. However, a breakdown of the tanking theory demonstrates that losing in the short-

term in the NBA may be beneficial to long-term wins, and therefore long-term profit.

Agents always want to maximize their revenue and minimize their costs to optimize

profit. For simplicity, this paper refers to the agents as the owner, general manager, and head

coach of each team respectively. Each of the three positions’ incentive is pursuing profit in the

long-run. Clearly it is in the owner’s interest to pursue profit in the long-run since that is

understandably his purpose of owning a professional team: making money. For the general

manager and the head coach, it is not as straightforward. The general manager and head coach

theoretically first maximize long-term wins. Once the team maximizes long-term wins, long-run

profit follows since the owner will likely reward the general manager and head coach for their

efforts with a multi-million dollar contract extension. Therefore, in a round-about-way, all three

positions – the owner, general manager, and head coach – have a strong incentive of pursuing

long-run profit.

In economic theory, profit maximization for a firm is typically measured by the following

equation shown in Equation 4.1:

Maximize rKwLpQ with respect to Q, L and K subject to ),( KLfQ

[4.1]

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Firms want to maximize profit by increasing total revenue (pQ), but labor (wL) and

capital (rK) costs constrain them. This paper builds off the theoretical framework of this formula

in order to explain why NBA teams engage in tanking behavior.

To begin, this paper assumes a two-period model. Teams want to maximize total revenue

in periods one and two, while minimizing costs in those same two periods. For the total cost

function in this model, it is assumed that capital is zero. Equation 4.2 displays the total cost

functions for both periods:

11 * LwTC

22 *LwTC

[4.2]

Therefore, the number of players on the roster and the salaries paid to each player only

determine the total costs. This paper assumes costs are fixed and total costs in Period One equal

total costs in Period Two. Future research could potentially examine if costs should be

incorporated into the model, but for simplicity the paper assumes fixed costs.

With costs fixed, the team’s incentive is maximize total revenue. Total revenue also has

one function in each period. If a team wins more games in Period One )( 1wins , that generates

more total revenue in Period One. The same is true for Period Two, and therefore total revenue is

a function of wins as shown in Equations 4.3:

)( 11 winsgTR

)( 22 winsgTR

[4.3]

The draft pick (draft) that a team receives is also a function of wins in Period One: the

fewer the wins, the higher the draft pick. Equation 4.4 demonstrates this function:

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)( 1winshdraft

[4.4]

The last function investigates wins in Period Two. A successful team that is winning

games in Period One has a good chance of winning games and success in Period Two, assuming

that most of the same players remain on the team. However, a bad team in Period One also has a

chance of success in Period Two, if they can acquire a superstar with a high pick in the NBA

Draft. Therefore, wins in Period Two )( 2wins is a function of how a team performs in Period

One and also its draft position (h(wins1)). It is defined in Equation 4.5 as:

))(,( 112 winshwinsfwins

[4.5]

The agents in the model want total revenue maximization in periods one and two combined.

Therefore, they are willing to lose more and take less in Period One by tanking if that leads to greater

revenue in Period Two. It is necessary to take the derivative form of the functions and determine the

effect that winning games in Period One has on total revenue in both periods combined. The effect of

winning games in Period One on total revenue in Period One is simple as shown in Equation 4.6:

01

1

1

1

wins

g

dwins

TR

[4.6]

Equation 4.6 is positive, which determines that winning more games in Period One

increases total revenue for that period. However, breaking down the effect of total revenue in

Period Two is more complicated as displayed in Equation 4.7:

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)(*)(*)()(*)(

1

2

1

2

1

2

wins

h

h

f

f

g

wins

f

f

g

dwins

TR

>0 >0 >0 >0 <0

(1) (2)

[4.7]

Equation 4.7 exhibits the effect of a change in wins in Period One on total revenue in

Period Two. The first half of the equation indicates that an increase in wins in Period One

produces more wins in Period Two, which in turn leads to higher Period Two total revenue.

Therefore, the sign on the derivative for the first half of the equation should be positive. The

second half of the equation suggests that more wins in Period One leads to a worse draft pick,

which leads to lower wins in Period Two, and therefore, lower Period Two total revenue.

Therefore, the sign on the derivative for the second half of the equation is negative. In total,

Equation 4.6 and Equation 4.7 can combine to form Equation 4.8, which specifies the effect of a

change in wins in Period One on overall total revenue ( 21 TRTRTR ):

1

2

1

2

1

1

1

*)(*)()(*)()(wins

h

h

f

f

g

wins

f

f

g

wins

g

dwins

TR

(1) (2)

[4.8]

Tanking only increases total revenue when the first term of the equation is greater than the

second. This is further displayed in Equation 4.9, which suggests that winning fewer games in Period One

contributes more total revenue in both periods combined. Therefore, it is in the agents’ best interest to

tank in Period One only when the absolute value of the first term of the equation is greater than the

absolute value of the second term of the equation.

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1

2

1

2

1

1 *)(*)()(*)()(wins

h

h

f

f

g

wins

f

f

g

wins

g

(1) (2)

[4.9]

Moreover, if it is in the agent’s best interest to tank, this contrasts with the principal’s interest of

league-wide competitive balance. Conversely, if the first term in the equation is greater than the second

term in the equation in absolute value terms, the team’s incentive should be to win games in Period One

and thus, no principal-agent problem should occur.

Overall, the NBA wants competitive balance and has a certain set of rules that it wants its agents

(the league owners and management) to follow. However, the theory above demonstrates that some teams

may have an incentive of approaching long-term wins, and therefore long-term profit, in a different way

than the NBA initially designed. Strictly speaking, the system currently in place still allows teams to

operate within the rules, but not by the original design of the league. The following section analyzes

whether this hypothesis is true and if tanking does indeed contribute to long-term wins.

5. Data Summary

This paper collects data from the website Basketball Reference, a comprehensive

statistical website of the NBA. The primary purpose is determining how tanking affects long-

term wins. For this paper the long-term is defined as an average total (wins, for example)

between the player’s third season through his last season as a member of the team that drafted

him. The following page further examines the long-term. Therefore, teams that finished with a

bottom-six record in the NBA for a given season are automatically designated with a dummy

variable of tanking, whereas teams that did not finish in the bottom-six are not labeled as tanking

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for the given season. This assumption draws back to the literature review in Section 3 when

authors such as Taylor and Trogdon (2002) and Walters and Williams (2012) find substantial

evidence that losing teams tank, especially after elimination from playoff contention. The data

begins in the year 1994 since that is the first year that the NBA introduced the current lottery

system. Data is collected up until 2009 to determine the players drafted that season and their

effect on long-term wins.

If players drafted in the top six all played for the team that originally drafted them the

following season, then six more players are collected for the treatment group – teams that did not

finish in the top six. Players from teams that did not finish in the top six are randomly selected in

alphabetical order. If however, a top-six draft pick was traded to a team that finished outside the

top six in a previous season, then that player is not included as an observation; that team would

have no incentive to tank since their draft selection no longer belonged to them. For example, in

1997, the Grizzlies traded an unprotected1 2003 first round pick to the Pistons, which resulted in

the second overall pick in the draft that year. Since the Grizzlies did not own that selection, they

theoretically would have had no incentive to tank for that season; therefore, the Pistons selection

of Darko Milicic at the No. 2 spot is not included in the data set. Instead, the pertaining situation

only calls for five players in both the control and treatment group. In all, from the year 1994-

2009, there are 150 observations (75 tanking teams and 75 non-tanking teams) signifying that

150 players played for the team that drafted them the following season.

The variable longwin is the primary dependent variable in the model and examines how

many wins the team had in the player’s third season with the organization. If the player is on the

1 Unprotected signifies that a team can receive any draft pick via a trade, regardless if it is a lottery pick. Trades sometimes involve protected

lottery picks: If Team A trades a protected lottery pick the previous season to Team B, but Team A finishes with a poor record and ends up in the lottery after the season, then Team A holds on to that lottery draft pick.

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team that originally drafted him for more than three seasons, then the number of wins is averaged

for all seasons until his last season as a member of the team. For example, long-term wins for

LeBron James is the average number of wins for the Cleveland Cavaliers from 2005 (James’

third year on team) through 2010 (James’ last year on the squad). In a more general example, a

player on a team that wins 30 games in season three, 33 games in season four, and 36 games in

season five – but then signed by a different team after that fifth season – played on a team with a

long-term win total of 33 games. Obviously long-term wins are a great measure of determining a

team’s success, but only for the regular season. Therefore, the variable playoffs measures how

many playoff appearances that the player participated in the long-term on the team that drafted

him. The final dependent variable is winshare, which signifies a player’s long-term contribution

to the team. The variable winshare measures the percentage of a team’s wins that that player

accounts for by himself; it examines the overall effectiveness of the player and how he alone

contributes to long-term wins. Win shares is calculated through an advanced statistics method,

developed by Basketball Reference. The method is similar to the framework developed by the

famous baseball statistician Bill James. To measure win shares, Basketball Reference provides a

detailed formula that accounts for many variables such as points, offensive possessions, and a

player’s marginal offensive efficiency. The measure therefore calculates how many wins that

player alone contributed to his respective team that season. By taking the long-term win share

divided by long-term wins for the team, this estimates the variable winshare for this paper:

The first crucial independent variable in the model is AllStars. The variable AllStars

signifies the number of All-Star players that the examined player played with in his career by

season in the long-term. For example, Blake Griffin played with two others All-Stars in the long-

term – Chris Paul in 2012 and also in 2013. To control for other factors, this papers introduces

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two other independent variables. PER determines the long-term average player efficiency rating

for a player’s respective team, while also excluding the current examined player. Player

Efficiency Rating is a system designed by former ESPN statistician and current Memphis

Grizzlies Vice President of Basketball Operation John Hollinger, which examines a player’s

productivity on a per-minute basis. Therefore, this variable should control for team quality.

Payroll controls for a general manager and how he builds his team. General Managers that

highly pay their players likely have a better team and therefore, a larger long-term win total. The

final variable, tanking, is a dummy variable; as previously mentioned, it automatically assumes

that a team that finishes with a bottom-six record is described as tanking for that season. Table

4.1 in the Appendix displays a table that calculates the mean values for each variable.

The data appears on first glance that tanking is indeed a favorable phenomenon. Long-

term wins for a tanking team is greater by more than two wins, while tanking teams also are .3

times more likely of going further in the playoffs. The number of players that generate no long-

term wins is greater for non-tanking teams. However, this is likely because teams that draft

players higher may have a “larger leash” – this may be credited to loss aversion. Since teams

invest a high draft selection on the player, they are therefore less willing to cut their losses

because they believe the player still has a high potential. Thus, teams that draft high are less

willing to give up on a top draft pick compared to a team that drafted a player in the mid-to-late

first round who does not have as high of a potential. The biggest discrepancy in the table is likely

the win share, where the player from a tanking team generates on average 11.87% of the team’s

wins in the long-term, while a player from a non-tanking team only accounts for 6.4% of his

respective team’s long-term wins. Tanking teams have fewer All-Stars in the long-term on

average because it is more likely that the player that they drafted in the top six is already an All-

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Star, making it difficult for a second player on the same team to also become an All-Star. Lastly,

tanking teams on average likely have a lower payroll ranking because the general manager

originally assembled a low-salaried team with worse players, which eventually contributed to a

top draft pick. Regression analysis in the following section further detects if it is worth it to tank

and receive a higher draft pick for a potential higher win total.

6. Results

The Appendix displays full tables with results. In this section, this paper examines the

effect of long-term wins on tanking, since the theory predicts that tanking should contribute more

long-term wins. Model I is a simple model and observes the effect of only tanking on long-term

wins; the linear equation is described in Equation 6.1:

ii1i kingta+=longwin

Equation 6.1

Model II also employs long-term wins (longwin) as the dependent variable, but includes

the following independent variables: tanking (the dummy variable), PER, AllStars, and Payroll.

The linear equation for Model II is revealed in Equation 6.2:

iii3i2i1i +payroll+PER+AllStars+kingta+=longwin 4

Equation 6.2

As Table A demonstrates, predicted theory is not in accordance with the results. The

variable tanking is not significant in both models, and therefore tanking does not guarantee and

increase long-term wins for an NBA team. However, when examining Model II, both PER and

All-Stars are significant: if one more All-Star is added to the team, then that contributes to 1.26

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more wins in the long-term. If the overall team (outside of the examined player) increases its

average Player Efficiency Rating by a full point, then that contributes to an astounding 7.85 more

wins in the long-term. Therefore, the results can be interpreted in the following manner: long-

term wins are not fully dependent on tanking and which player a team receives in the NBA Draft.

Instead, the wins are determined based on how many other All-Stars are on a team and the

overall team quality, while excluding the player originally drafted.

The results from above are quite surprising, considering that tanking for a player has no

effect on the long-term wins of an NBA franchise. However, does that demonstrate that there is

absolutely no benefit to tanking? The same process from above projects the effect of tanking –

however, this time on the drafted player and his individual contribution to wins (winshare).

Model III examines whether only tanking contributes to a better win share and the equation is

shown in Equation 6.3:

ii1i kingta+=winshare

Equation 6.3

Likewise, Model IV also investigates the effect of tanking on a player’s contribution, but

also include AllStars, PER, and payroll, just like Model II. The linear equation is displayed

below in Equation 6.4:

iii3i2i1i +payroll+PER+AllStars+kingta+=winshare 4

Equation 6.4

When winshare is modeled solely by tanking in Model III, notice that the tanking

variable is significant: a player on a tanking team accounts for 5.5 percentage points more of his

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team’s wins compared to a player on a non-tanking team. Thus, if Team A tanks for a higher

draft pick, the player that Team A drafts contributes 5.5 percentage points more (by himself) to

his team’s win total compared to an otherwise contribution on Team B, a non-tanking team.

When the approach applies the following control variables in Model IV, tanking is still a

significant variable. The results in this instance show that a player on a team that tanks

contributes 6.6 percentage points of more wins to his team compared to players on a similar team

that did not tank during the given season. The other variables are not significant, which makes

sense considering that the model is examining an individual player’s win share and overall

contribution to his team. Therefore, the results present that if a team tanks, they are drafting a

better player, or a player that – at the very least – contributes more wins to a tanking team than a

non-tanking team.

Thus far, the two tables establish that, while tanking does not contribute to long-term

wins, a team is still more likely to draft a better player by tanking. The last dependent variable

examined is playoffs, which totals the number of playoff appearances that player made in the

long-term on the team that originally drafted him. Model V in the Appendix displays the results

when playoffs is the dependent variable. Equation 6.5 shows the effect on playoff appearances

when tanking is the single independent variable:

ii1i kingta+=playoffs

Equation 6.5

In addition, Model VI presents the results when the approach adds the other three

dependent variables. Equation 6.6 below reveals the form of the linear equation:

iii3i2i1i +payroll+PER+AllStars+kingta+=playoffs 4

Equation 6.6

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When tanking is the only independent variable in Model V, the dummy is significant and

demonstrates that a player on a team that tanks likely makes .43 more postseason appearances

than a player on a non-tanking team. When the approach includes the other independent

variables in Model VI, tanking is again significant; however, this time a player on a tanking

team is destined for the playoffs almost one more time in his career (.888) with the team that

originally drafted him compared to a player on a team that did not tank. In addition, AllStars is

again also a significant variable, and exhibits that if a team pairs their drafted player with an All-

Star, they increase their playoffs appearances in the long-term by .57 appearances. That is, if a

player is paired with another All-Star, then he appears in the playoffs .57 more times than a

player that is not paired with an All-Star caliber player.

Since tanking and AllStars are significant, this signifies that a team is more likely to make

the playoffs if it tanks and/or acquires other All-Star players and pairs them with the player that

the team tanked for. However, when tanking is run on long-term wins (with and without other

explanatory variables), it indicates that tanking is not a significant variable. This is a rather

curious case because the more long-term wins, the more likely a team is going to make the

playoffs. Therefore, this must exemplify that while teams that tank more likely make the

playoffs, they are likely just “squeaking in” as a lower seed (No. 6, 7, or 8 seed) in the

postseason by winning about 40-50 games per season. Another plausible explanation is that it

could take more than three years for a player to turn the franchise around; therefore, a lower win

total may ensue in Yr. 3 or even Yr. 4 (where a team may miss the playoffs altogether) for the

team before finally transforming into a consistent playoff contender.

Overall, the analysis indicates that teams that tank more likely receive a better player in

the draft, yet this still does not guarantee that a team will win many games in the long-term.

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When the team performance is determined as the dependent variable (longwins and playoffs),

AllStars is significant in both cases, signaling that a team should pair as many All-Star caliber

players to its team in addition to the player that it tanked to draft. Of course, a team can rarely go

out and easily acquire an All-Star caliber player, which makes a general manager’s job all the

more difficult. Yet, since PER is significant as well Model II, a team’s intention should also be

acquiring high efficiency players at a reasonable cost. In addition, by building a roster with high-

efficiency players, those players can eventually be traded to a team for an All-Star player that is

otherwise rather difficult to acquire. The analysis further signifies that a player cannot do

everything by himself; one superstar is not able to individually carry a team for the long-run. A

player that the tanking team drafted will make more playoff appearances, but the ultimate

success is determined based on how team management builds the rest of the team around that

given player.

The most prominent example is arguably the Minnesota Timberwolves with Kevin

Garnett in the late ’90s and early 2000s. While Kevin Garnett – a 10-time All-Star with the

Timberwolves – helped his team post multiple playoff appearances, the Timberwolves lost in the

first round of the playoffs an NBA record seven consecutive times (Associated Press, 2004).

After finally making the Western Conference Finals and breaking the drought, Garnett failed to

make the playoffs in his final three seasons with the Timberwolves and finished with a long-term

average win total of only 40.2 wins. To this day, conversation still ensues by sports fans in the

Minneapolis-St. Paul area who argue that Timberwolves general manager Kevin McHale never

paired Garnett with another star player. In another example, LeBron James, considered the best

basketball player in the world, never won a championship with the Cleveland Cavaliers, despite

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aiding his team to an average long-term win total of 54.4 wins; general manager Danny Ferry

never paired LeBron with another All-Star caliber player.

7. Conclusion

The overall purpose of this paper examined if teams better themselves by tanking rather

than pursue winning a championship for that given season. This paper does not find evidence

that supports the original hypothesis, where tanking directly contributes to long-term wins.

However, tanking is still a preferred phenomenon when considering how team management

should approach a given season. The paper shows that while acquiring efficient players and All-

Stars to surround the drafted player is the most important facet of management’s pursuit of long-

term wins, tanking still contributes to a better overall player in the draft and more postseason

appearances in the long-term.

Thus, while there is more to success in the NBA than just tanking, the strategy is still an

important and beneficial aspect to team management when determining how to build a roster for

the future. Consequently, when looking at the overall organization of the National Basketball

Association, this paper does prove that there is indeed a principal-agent problem. While the NBA

desires each team competitively pursue winning a championship, the teams pursue the action in a

round-about-way. Since tanking is a huge puzzle piece to long-term success and competitiveness,

a team can tank and be poor for a few years and then have the drafted player eventually carry

them into the playoffs in the long-run. There is an apparent misalignment of incentives originally

designed by the league, which needs correcting if the product wants to remain sustainable in the

future.

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Many economists and journalists propose many solutions to potentially fix the current

NBA Draft lottery system. However, with each proposition there is often a drawback. The most

popular solution comes from Ian Gold, a Ph.D. candidate at the University of Missouri, who

proposed his method at the 2012 MIT Sloan Sports Analytics Conference. In what is known as

the “Sloan Solution”, the NBA awards the number one overall pick to the team that wins the

most games after elimination from playoff contention (Wade, 2012). This proposal slightly

reduces tanking, but teams still have an incentive of losing as many games as quick as possible

during the regular season to become the first team that is mathematically eliminated; that team

then has a head start on other teams by having more opportunities at recording victories. As

recent as December 2013, NBA Commissioner Adam Silver openly discussed a lottery wheel

concept, in which the draft lottery is replaced with a revolving wheel; each team is given a draft

slot for the next 30 years and that team drafts one selection higher relative to the previous year

(Young, 2014). If the NBA wants the entire elimination of tanking, this is arguably the best

approach. However, the draft’s main purpose is increasing competitive balance, and this

proposed solution certainly reduces that aspect. For example, some teams could do well the

previous year but also select a good player near the top of the draft following the season.

Conversely, some poor teams would select near the bottom and draft a worse player.

One limitation to this study is that it is difficult to assume a team is tanking for a season

even if they finish with a terrible record. Although previous literature examines that once a team

is eliminated they have a strong incentive to tank, it is impossible of knowing if a team tanks

from the beginning of the season. One possible study for future research that could correct this

flaw is by examining the mechanisms by which teams tank: how often the head coach plays his

starters, how many players a general manager traded in the last year for draft picks, how many

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young players are on the roster, and how many star players miss a game from an “injury.”

Determining the mechanisms and identifying which teams actually tank would certainly improve

this paper’s study; in addition, it could open further avenues that have gone unnoticed by past

researchers in the topic of tanking behavior in the NBA.

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A. Appendix

Table A

Summary Statistics – Mean values

Variables Tanking teams Non-tanking teams

Long-term wins 29.64 27.52

Playoffs 1.52 1.09

Win Share 11.87% 6.4%

All Stars 1.87 2.88

Payroll 17.1 15.6

Player Efficiency Rating 14.43 14.63

# of players that generated

no long-term wins

18 24

Table B

Dependent Variable - Long-Term Wins

Variable Model I Model II

tanking 2.123

(0.531)

1.078 (0.488)

AllStars 1.265

(0.000)***

PER 7.854

(0.000)***

payroll -.1496

(0.283)

constant -74.417

(0.000)***

Adjusted R-squared -.004 .504

DF 148 100

F-Stat 0.39 27.41

N 150 105

P-Values are displayed in parentheses. * indicates significant value at 10% level, ** indicates

significant value at 5% level, *** indicates significant value at 1% level.

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Table C

Dependent Variable – Win Share

Variable Model III Model IV

Tanking .055

(-.015)**

.065 (0.000)***

AllStars .001

(0.723)

PER -.003

(0.828)

payroll -.001

(0.715)

Constant .139

(0.478)

Adjusted R-squared .0806 .111

DF 148 100

F-Stat 4.07 4.24

N 150 105

Table D

Dependent Variable – Playoffs

Variable Model V Model VI

Tanking .427

(.000)***

.88894 (0.003)***

AllStars .57553

(0.000)***

PER .16666

(0.458)

payroll -.03928

(0.130)

Constant -1.606

(0.646)

Adjusted R-squared .005 .524

DF 148 100

F-Stat 1.81 29.6

N 150 105

P-Values are displayed in parentheses. * indicates significant value at 10% level, ** indicates

significant value at 5% level, *** indicates significant value at 1% level.

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Multiple All-NBA Performers (1990-2010)

All-NBA Players Number of All-NBA teams Draft Selection Year

Derrick Coleman 2 1 1990

Shaquille O'Neal 14 1 1992

Chris Webber 5 1 1993

Allen Iverson 7 1 1996

Tim Duncan 13 1 1997

Yao Ming 5 1 2002

LeBron James 8 1 2003

Dwight Howard 6 1 2004

Blake Griffin 2 1 2009

Gary Payton 9 2 1990

Alonzo Mourning 2 2 1992

Penny Hardaway 3 2 1993

Jason Kidd 6 2 1994

Shawn Marion 9 2 1999

Kevin Durant 3 2 2007

Grant Hill 5 3 1994

Stephon Marbury 2 3 1996

Pau Gasol 3 3 2001

Carmelo Anthony 5 3 2003

Deron Williams 2 3 2005

Dikembe Mutombo 3 4 1991

Chris Paul 4 4 2005

Russell Westbrook 3 4 2008

Kevin Garnett 10 5 1995

Ray Allen 2 5 1996

Vince Carter 2 5 1998

Dwayne Wade 7 5 2003

Brandon Roy 2 6 2006

Vin Baker 2 8 1993

Tracy McGrady 7 9 1997

Dirk Nowitzki 12 9 1998

Amare Stoudemire 5 9 2002

Paul Pierce 4 10 1998

Kobe Bryant 14 13 1996

Steve Nash 7 15 1996

Jermaine O'Neal 3 17 1996

Tony Parker 2 28 2001

Gilbert Arenas 3 30 2001

Manu Ginobili 2 57 1999

Ben Wallace 5 Not Drafted 1996