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00 03 Rumor

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Last Updated: 08 December 2020

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00 03 Rumor

Twitter@qvietrvmor

Kelly Oubre Jr. Has played well since coming to Phoenix, Last season he was the Suns ' second-leading scorer with 18. 7 points per game. He fills the important wing role between Devin Booker up top and Deandre Ayton in paint. He's also entering the last year of his current contract at $14. 4 million, and he will get a big raise after that. With Booker already having get max contract and Ayton up for one in a couple of years, plus Ricky Rubio making $34. 8 million over the next two seasons, Oubre may be too expensive for Phoenix to keep. Which has led to a lot of trade rumors. Enter the Golden State Warriors, who have an interest in Oubre, something ESPN Nick Friedell talked about on Jump. Warriors need more wings, particularly ones who can defend and hit three. They have Klay Thompson and Andrew Wiggins who will start, then Eric Paschall off the bench, but after that things drop off quickly. Golden State has a $17 million trade exception created when Iguodala was traded to Memphis, meaning Golden State could trade for Oubre and just send back pick. There would be no need to match salaries. Why would Phoenix do it is the question. The Suns want to make a playoff push this season. Their 8-0 bubble run gives them confidence and Oubre would be a big part of that. Behind Oubre is Mikal Bridges and maybe Cameron Johnson, but those are considerable drop-offs from what Oubre brings. The Warriors do have a wing player to trade back, even No. 2 pick nets player who needs development and time to contribute. Which is to say, Golden State may want to do this, but would Phoenix? It is something to watch as we head into trades around the 2020 NBA Draft on Nov. 18, but do bet on this rumor becoming reality.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Rumors for the Day:

Dana White told TMZ he would be interested in booking Brock Lesnar vs. Jon Jones, but expectation remain that Lesnar will come back to WWE. WrestleVotes say there has been some discussion within WWE of moving NXT off Wednesday nights. The Observer notes that Rey Mysterios injury isnt serious and the belief is he wo need surgery and may only miss a couple of months. According to PW Insider, Nigel McGuinness is expected to return to work for WWE soon. When MLW returns to TV it will have a new name and will not debut on Saturdays on BeIN Sport but rather primetime slot on weekdays on Fubo Sports Network, per Insider. If you have heard of any interesting rumors that you would like to add, feel free to post them in the comments section below. Just remember they are rumors and not confirmed as fact, so please take them as such. And check our weekly Rumor Look Back here to keep track of how often Rumor turn out to be correct.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Introduction

With the development of the Internet, rumor spreading has become easier and faster 1 2. The social networking service IS the main platform for Rumor spreading due to its numerous users and complex network structure. Everyone on social networks IS both spreader and recipient of information. So, rumors are easily produced and spread widely, and some rumors cause great panic in society 3 4. For example, rumor that nuclear leakage caused by the Fukushima nuclear accident in Japan would pollute salt was widely spread on the Internet in 2011 and caused panic buying of salt. Since rumor spreading can cause serious consequences, IN-depth - investigation of rumors spreading in complex Social networks has significance and can help governments or managers of SNS to control rumor spreading. The process of rumor spreading in social networks IS similar to the process of epidemic spreading; thus, most studies of rumor spreading are based on the epidemic model. Daley and Kendall formalize first rumor spreading model, namely, DK model, in 1964; model IS based on the classic epidemic model SIR 6. In the DK model, crowd was divided into three groups: people who have not heard rumors, people who spread rumors, and people who stop spreading rumors. With studies of complex network theory, effect of complex network structure on the process of rumor spreading was explore. Moreno et al. 7 introduced mean field equations to characterize the rumor spreading process in complex networks. Nekovee et al. 8 contrasted Rumor spreading process in a random network with Rumor spreading process in a scale-free network, Results show that the Rumor spreading model exhibits different spreading thresholds in different networks. In addition, rumor spreading IS a social contagion process, in which people's behaviors and social environments may influence the process of rumor spreading. Thus, some researchers consider people's behaviors and social environments in rumor spreading. Zhao et al. Propose Rumor spreading model that consider forgetting mechanism, which express that spreaders may convert to stiflers without contacting others. These researchers also verify that forgetting rate, which change over time, has significant impact on Rumor spreading 9 10 11. Corresponding to forgetting mechanism, some scholars consider remembering mechanism and yield to SIHR model 12 13. Wang et al. 14 believe that trust between people can influence rumor spreading and propose a rumor spreading model that considers trust mechanism to analyze the influence of trust between people on rumor spreading. Additionally, two or more rumors would spread at same time. Some scholars extend classic single rumor spreading models to coupled spreading models, which consider two rumors 15 16 17. Moreover, some people may refute rumors that he or she has hear. In light of this, Zan et al. 18 focus on counterattack mechanisms and analyzed the influence of networks ' self-resistance on rumor spreading. Similarly, Zhang et al.

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* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Rumor spreading model

In the above analysis, we have obtained spreading thresholds of SEIsIrR model in homogeneous networks and heterogeneous networks, which are express in Eq. And Eq. Comparing these two equations, we can obtain {matheq}{R}_{0}=\frac{\overline{k}(x\mu +y)\gamma \alpha \lambda }{{\eta }_{2}}{endmatheq} in both homogeneous networks and heterogeneous networks As 1 approaches 0. Therefore, results conclude that basic reproduction number in heterogeneous networks is equivalent to that in homogeneous networks when 1 approaches 0. Basic reproduction numbers depend on average degree of network rather than degree distribution of network When rate of individuals in class S who switch to class R approaches 0. To further analyze the numerical relationship between R 0 in homogeneous network and R 0 in heterogeneous network, we can obtain other forms of R 0 in homogeneous networks and heterogeneous networks by further conversion of Eq. And Eq., As show in Eq. And Eq., Respectively: to make R 0 in homogeneous networks comparable to R 0 in heterogeneous networks, we set the average degree {matheq}\overline{k}{endmatheq} of two networks to the same value. Because {matheq}\overline{k}=\sum _{k}kp(k){endmatheq} we can obtain {matheq}kp(k) < \overline{k}{endmatheq} comparing Eq. And Eq., R 0 in heterogeneous networks is less than R 0 in homogeneous networks. This result shows that the spreading threshold of heterogeneous networks is less than that of homogeneous networks when parameters and average degree of networks are equivalent in both networks. In other words, rumors spread more widely in homogeneous networks.

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* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Verification and numerical simulation

To maintain consistency of the analysis process, simulation process in this subsection adopts network generation parameters in sub-subsection title Verification by numerical simulations. Fig. 6 shows evolution of densities of individuals in classes E, S and R over time with different in WS network. As shown in Fig. 6, As increases, peak value of {matheq}{\rho }^{E}(t){endmatheq} decreases when it > 0. 5 Conversely, peak value of {matheq}{\rho }^{E}(t){endmatheq} increases with increase in When < 0. 5 When a rumor is fully credible or completely untrustworthy, no hesitation will occur. When is approaching 0. 5, rumors become more confusing and more people will doubt rumor. Therefore, maximum value of {matheq}{\rho }^{E}(t){endmatheq} will appear when = 0. 5 As shown in Fig. 6, peak value of {matheq}{\rho }^{S}(t){endmatheq} is positively correlated with γ. With increase in γ, time for {matheq}{\rho }^{S}(t){endmatheq} to attain its peak decreases. As shown in Fig. 6, larger value of Is, larger final value of {matheq}{\rho }^{R}(t){endmatheq} Is and shorter time to attain steady state Is. Thus, we can conclude that increase in will cause an increase in speed and final scale of rumor spreading. Fig. 7 illustrates changes in densities of individuals in classes E, S and R over time with different in BA network. Compared with Fig. 6, similar changes occur in the WS network, that is, maximum densities of individuals in class E appear when is 0. 5 refers to Figs. 6 and 7. However, curves with different in BA network are closer to each other than those in WS network refer to Figs. 6 and 7, which mean differences in time to attain their peak values with different in BA network are smaller than those in WS network. Central nodes in the BA network accelerate the spread of rumors and reduce the influence of an increase in rumor spreading. Fig. 8 describes changes in densities of individuals in classes E, S and R over time with different {matheq}{\rho }^{Ir}(0){endmatheq} in WS network, where {matheq}{\rho }^{Ir}(0){endmatheq} represents initial proportion of individuals in class Ir. Since classes E and R have no individuals at initial time, that is, {matheq}{\rho }^{E}(0)={\rho }^{R}(0)=0{endmatheq} {matheq}{\rho }^{Is}(0)+{\rho }^{Ir}(0)+{\rho }^{S}(0)=1{endmatheq} because {matheq}{\rho }^{E}(t){endmatheq} sum of {matheq}{\rho }^{E}(t){endmatheq} and {matheq}{\rho }^{E}(t){endmatheq} is a definite value, that is, give value of {matheq}{\rho }^{E}(t){endmatheq} initial population classification is determine. {matheq}{\rho }^{E}(t){endmatheq} is set to 0. 3995 0. 4995 and 0. 5995, which means that the initial number of individuals in class Ir is less than, equal to and greater than the initial number of individuals in class Is. As shown in Fig. 8, smaller {matheq}{\rho }^{E}(t){endmatheq} Is, higher peak of {matheq}{\rho }^{E}(t){endmatheq} Is. Conversely, larger {matheq}{\rho }^{E}(t){endmatheq} Is, higher peak of {matheq}{\rho }^{E}(t){endmatheq} Is and the larger final value of {matheq}{\rho }^{E}(t){endmatheq} Is.

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* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

5. JRUE HOLIDAY

Trade rumors sprouting up about New Orleans Pelicans guard Jrue Holiday is nothing new, as the talented floor general has been highly bandy-about trade targets dating back through last season-and even earlier than that. However, this week, we got our most concrete bit of reporting on potential Holiday trade when Athletics Shams Charania dropped following on us early on Wednesday: with the Pelicans fully committed to youth movement and Lonzo Ball needing longer look as the team's primary playmaker, it makes some sense why New Orleans would want to see what they might be able to get in theoretical Holiday trade. That doesnt mean hes lock to get dealt, with but this latest rumor is certainly more substantial than anything weve heard on the holiday front in months. Holiday, one of most underrated point guards in the game, averages 19. 1 points, 6. 7 assists and 1. 6 steals last season and is under contract for two more seasons, second of which he has a player option on, meaning he could hit free agency next year, in the 2021 offseason. Holiday projects to be merely 42 highest-pay player in the NBA next year, more than fair price for his level of production. Some of teams who have been tied to Holiday for potential trade in the past have been the Miami Heat and Denver Nuggets, so keep an eye on them, among other contenders, here as this situation unfold.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

4. TRISTAN THOMPSON

Tristan Thompson to Celtics rumors have been prevalent for quite some time, and now it appears the sides have finally reached agreement. Chris Hayes of Yahoo! Sports reported Saturday that Thompson is heading to Boston. Athletic's Joe Vardon added that the deal is for two years, $19 million. Thompson, No. 4 pick in the 2011 NBA Draft, has an average of 9. 4 points, 8. 7 rebounds, and 0. 7 blocks per game throughout his career. This past season, his scoring average rise to a career-high 12 points per game. He was a key role player on the Cleveland Cavaliers 2016 championship team, and is widely considered one of the more dependable defensive-mind bigs in the league. The Celtics have had player average 10 rebounds per game since Al Jefferson in 2006-07, according to ESPN Stats & Info. That streak of 13 seasons is the longest active drought in the NBA. Hes known in Boston as a player who has dominated the Celtics on glass over the years. Thompson will certainly be in the rotation, and theres chance he could compete for the starting center spot with Daniel Theis. According to NBC Sports Bostons Chris Forsberg, news means that the Celtics plan to stay out of tax this year. The team also has $3. 6 million biannual available to sign talent above minimum, Forsberg write. The team could also explore trade options with recently generated exceptions for Kanter and Poirier. Thompson, 29, has appeared regularly on Keeping Up with Kardashians and has a child with Khloe Kardashian.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

3. DENNIS SMITH JR.

NBA Preseason Stats - Advanced Stats

SeasonTeamGPGSTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2017-18DAL55.479.4713.2311.307.1638.6315.822.941.8428.11143.664.171.0097.4102.018.94
2018-19DAL44.580.5544.707.266.0631.4914.401.811.7724.98151.423.551.35114.5106.121.68
2019-20NYK22.317.2065.9615.2310.6923.5822.621.813.4521.19109.31-0.7673.2103.75.62

Dennis Smith Jr. Will return to the Mavericks on Tuesday and apparently be back in the Dallas rotation. This follows a bizarre week-long absence amid trade rumors, fugazy injuries, and hurt feelings. It is impossible to know what is really going on here. Mavs coach Rick Carlisle has been shockingly tender in his comments about Smith since the announcement he was coming back, even apologizing to Smith, which is not something one typically associate with Carlisle or the Mavericks organization. That tells me that the Mavericks screwed something up here and are trying to salvage a situation thats gotten out of hand. Regardless of what actually happened here, fact that Carlisle and the Mavericks have alienated their No. 9 pick from two drafts ago enough that he go into exile for a week spell out case that somewhere along the way, Dallas brain trust screwed up. Whether that was in drafting DSJ in the first place or in mismanaging his transition into life alongside Luka Doncic, it bad. As Josh Bowe notes at Mavs Moneyball, Dallas doesnt exactly have many blue chip youths to rely on beyond Doncic and Smith. Perhaps in another year or two this will look like funny little blip, footnote in Mavericks rise to excellence behind Doncic. A Superstar can paper over a lot. But it sure is curious right now, even with DSJ coming back.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

NBA Preseason Stats - Misc Stats

SeasonTeamGPGSDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2017-18DAL55000000.2232.100.600.1032.6000.060.100.16
2018-19DAL44000000.2702.110.440.3722.5000.320.070.39
2019-20NYK22000000.1581.500.330.4702.000-0.160.05-0.11

NBA Preseason Stats - Per Game

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2017-18DAL5519.44.0010.20.3921.603.60.4440.601.00.6000.602.002.604.201.200.401.202.0010.20
2018-19DAL4425.95.5011.50.4781.754.50.3892.754.25.6471.001.752.754.751.000.502.502.2515.50
2019-20NYK2226.11.508.50.1760.503.00.1673.004.00.7501.504.005.504.501.001.003.003.006.50

NBA Preseason Stats - Totals

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2017-18DAL5596.82051.392818.44435.60031013216261051
2018-19DAL44103.72246.478718.3891117.6474711194210962
2019-20NYK2252.2317.17616.16768.75038119226613

NBA Regular Season Stats - Advanced Stats

SeasonTeamGPGSTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2017-18DAL6969.473.4462.6211.717.0329.2714.891.730.7928.71140.152.211.0293.5110.212.71
2018-19All Teams5350.502.4792.318.385.4327.3717.652.131.1224.72138.440.931.1197.0109.212.35
2018-19DAL3232.527.5002.248.935.7524.0320.282.111.0223.10147.89-0.881.1697.2108.111.75
2018-19NYK2118.473.4542.397.534.9632.9214.232.171.2727.18126.973.701.0596.6110.913.58
2019-20NYK343.399.3794.0911.947.8726.9519.842.531.4123.10114.701.380.8983.8112.97.46

NBA Regular Season Stats - Misc Stats

SeasonTeamGPGSDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2017-18DAL6969610004.2881.850.370.192247.319-2.241.69-0.55
2018-19All Teams5350210006.2621.640.440.242132.396-1.011.770.76
2018-19DAL3232110005.2381.380.400.231616.500-0.461.110.64
2018-19NYK2118100001.2992.110.500.25516.238-0.490.590.10
2019-20NYK343000003.1271.690.480.25727.206-1.350.38-0.97

NBA Regular Season Stats - Per Game

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2017-18DAL696929.75.8614.84.3951.544.91.3131.942.80.6940.723.043.775.191.030.262.202.8115.19
2018-19All Teams535028.55.2512.26.4281.263.92.3221.872.94.6350.602.322.924.751.260.382.432.9113.62
2018-19DAL323228.44.9111.16.4401.343.91.3441.782.56.6950.562.473.034.311.250.342.563.1212.94
2018-19NYK211828.65.7613.95.4131.143.95.2892.003.52.5680.672.102.765.431.290.432.242.5714.67
2019-20NYK34315.82.126.21.3410.471.59.2960.791.56.5090.621.682.292.880.820.241.911.715.50

NBA Regular Season Stats - Totals

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2017-18DAL69692049.14041024.395106339.313134193.6945021026035871181521941048
2018-19All Teams53501508.5278650.42867208.32299156.635321231552526720129154722
2018-19DAL3232908.3157357.44043125.3445782.695187997138401182100414
2018-19NYK2118600.2121293.4132483.2894274.5681444581142794754308
2019-20NYK343537.472211.3411654.2962753.509215778982886558187

NBA Summer League Stats - Advanced Stats

SeasonTeamLocationGPGSTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2017-18DALLas Vegas66.582.5214.0916.7210.2129.0115.983.910.6628.54150.78-0.201.49110.892.027.84
2018-19DALLas Vegas22.466.4175.492.624.3146.7616.256.05-25.36152.886.341.00102.398.022.30

NBA Summer League Stats - Misc Stats

SeasonTeamLocationGPGSDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2017-18DALLas Vegas66000000.3111.470.760.6351.8330.640.371.01
2018-19DALLas Vegas22000000.3562.401.200.1711.5000.120.060.17

NBA Summer League Stats - Per Game

SeasonTeamLocationGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2017-18DALLas Vegas6625.95.3311.67.4571.504.33.3465.177.33.7051.003.834.834.172.170.171.502.8317.33
2018-19DALLas Vegas2223.34.5012.00.3751.006.50.1542.002.001.0001.500.502.006.003.000.003.502.5012.00

NBA Summer League Stats - Totals

SeasonTeamLocationGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2017-18DALLas Vegas66155.33270.457926.3463144.7056232925131917104
2018-19DALLas Vegas2246.5924.375213.154441.00031412607524

Non-FIBA Events Stats

YearEventGPMINFGMFGAFG%3PM3PA3P%FTMFTAFT%TRBASTSTLBLKPFTOVPTSPlace
2016adidas Nations Counselors317:204.39.3.4640.30.7.5000.31.0.3335.33.72.00.02.32.09.3-
2015adidas Nations530:126.411.8.5420.41.8.2222.84.0.7006.27.62.20.03.03.816.0Silver
2014adidas Nations521:485.47.6.7110.82.2.3642.42.6.9232.64.83.00.81.81.214.0Gold
* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

2. KARL-ANTHONY TOWNS

Devin Booker and Karl-Anthony Towns grow close as teammates at Kentucky for season. The Phoenix Suns have come up in chatter as a possible trade destination for Towns after a report from Athletic's Ethan Strauss that NBA team executives relayed word that Towns is unhappy in Minnesota. Strauss' report said that the Golden State Warriors were monitoring the situation with Towns, considered one of the elite centers in the NBA. Towns, 24, is averaging 26. 5 points, 11. 7 rebounds and 4. 4 assists per game this season. He agreed to a five-year, $190 million super-maximum contract extension last year. The Suns have previously come up in trade speculation surrounding the Timberwolves star and that speculation started up again after Strauss' report. SB Nation speculate about what Phoenix could potentially give up in a deal for Towns. Samuel Cooper write: Suns could make as hard a push for Towns as any team in the League. Obviously, any potential package for a superstar like KAT would revolve around last year's first overall pick, Deandre Ayton. But from there, Suns have many different routes they could go. They could toss in Kelly Oubre Jr. To help match salaries, and even entice Minnesota with a couple of extra first-round picks from there. The point is that if Towns really do hit the trade market sooner rather than later, virtually every team in the League will compete for his services. But with-list prospects like Ayton and a plethora of future picks and other assets, Suns have the potential to build as interesting a package as perhaps any other team. Fansided look at four trades the Suns could possibly make for center. Adam Stratton write: First and foremost, deal would most certainly require the inclusion of Deandre Ayton. Hes still on his rookie contract for three more years, so the value there is extremely strong. While Ayton hasnt exactly been lucky Doncic, he certainly hasnt been bust either, and on the surface, swapping those two makes sense. They are both big men who can shoot, and they were both number one overall picks of their respective draft classes. Fansided also take look at why the Suns aren't coming up more often in Towns trade chatter. Adam Maynes write: Phoenix Suns fans should be worried that the Warriors might have interest in acquiring Karl-Anthony Towns; that he could be paired up with DAngelo Russell; and that such a pairing could mean the end of Devin Booker in the Valley. But they could also look at rumor with promise that it might just be push that jam Jones and Robert Sarver need, to get a deal as superstar, and two-time all-star center. Valley of Suns write piece on how and why Towns would become member of the Suns. Sammy Cibulka write: like Booker, Towns has never really had great support around him and has been in losing culture most of his career.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

1. DEWAYNE DEDMON

G League Regular Season Stats - Advanced Stats

SeasonTeamGPGSTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2013-14SCW1515.558.54013.8227.8920.906.0213.172.186.1019.29122.54-3.671.21112.497.519.99
2015-16ERI11.594.53823.9739.9031.370.0012.250.006.6219.23153.85-6.041.31126.993.128.55

Dewayne Dedmon's rocky tenure with the Kings has come to an end. NBC Sports California's James Ham has confirmed that Sacramento is trading Dedmon and two second-round draft picks to the Atlanta Hawks for Jabari Parker and Alex Len. ESPN's Adrian Wojnarowski and Zach Lowe were first to report News of Trade, citing sources. Dedmon didn't see eye-to-eye with Kings coach Luke Walton, and received several DNP-CD's after demanding a trade last month. It is unclear how much more playing time he'll get in Atlanta, after the Hawks acquired center Clint Capela from the Houston Rockets on Wednesday. Programming Note: 2020 NBA Trade Deadline Show is coming your way this Thursday at 11: 30am on MyTeams app and on nbcsportsbayarea. Com! Our NBA Insiders will analyze all of the news and rumors that could impact the Kings heading into the Noon Deadline.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

G League Regular Season Stats - Misc Stats

SeasonTeamGPGSDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2013-14SCW15151100100.2020.610.740.1996.6000.741.181.92
2015-16ERI11100101.1940.000.000.2301.0000.110.100.21

G League Regular Season Stats - Per Game

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14SCW151533.76.8012.60.5400.000.07.0001.602.33.6864.479.1313.601.271.532.334.602.0715.20
2015-16ERI1134.37.0013.00.5380.000.00.0003.003.001.0009.0013.0022.000.000.003.004.002.0017.00

G League Regular Season Stats - Totals

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14SCW1515505.2102189.54001.0002435.686671372041923356931228
2015-16ERI1134.3713.53800.000331.000913220034217

NBA Preseason Stats - Advanced Stats

SeasonTeamGPGSTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2013-14GOS50.418.42111.1230.2419.956.6722.802.757.4221.3475.44-8.680.8974.882.213.23
2014-15ORL71.451.39313.9128.2821.242.1212.351.476.7513.1398.11-2.721.14100.988.315.30
2015-16ORL72.463.39113.3626.5719.742.8621.53-9.1915.17103.42-6.381.1791.591.813.03
2016-17SAN51.422.3335.9716.5511.462.0520.190.644.0216.7994.87-6.811.1177.595.62.78
2017-18ATL55.624.5976.8926.7816.7710.3921.112.045.9719.25158.06-6.021.35100.892.419.34
2019-20SAC44.579.5687.9930.2418.685.5422.442.784.2315.47147.12-6.071.2796.897.613.79

NBA Preseason Stats - Misc Stats

SeasonTeamGPGSDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2013-14GOS50000000.0560.330.500.1623.400-0.110.160.05
2014-15ORL71100001.0490.400.800.6143.5710.090.360.45
2015-16ORL72000000.0440.250.000.6152.714-0.010.240.22
2016-17SAN51000000.0410.170.170.7232.600-0.170.200.03
2017-18ATL55000000.1380.560.440.1923.4000.090.200.30
2019-20SAC44000000.0820.430.710.2322.5000.020.130.15

NBA Preseason Stats - Per Game

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14GOS5010.21.603.80.4210.000.00.0000.200.60.3331.202.804.000.400.601.002.001.203.40
2014-15ORL7119.11.574.00.3930.000.00.0001.432.43.5882.435.147.570.290.571.572.710.714.57
2015-16ORL7215.51.293.29.3910.000.00.0001.292.00.6432.003.715.710.290.001.432.001.143.86
2016-17SAN5115.31.203.60.3330.000.00.0001.602.60.6150.802.403.200.200.200.803.201.204.00
2017-18ATL5519.03.606.20.5810.201.20.1671.001.20.8331.204.605.801.000.801.202.201.808.40
2019-20SAC4420.12.505.50.4551.253.00.4170.751.25.6001.505.256.750.751.251.003.751.757.00

NBA Preseason Stats - Totals

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14GOS5051.2819.42100.00013.3336142023510617
2014-15ORL711341128.39300.0001017.588173653241119532
2015-16ORL72108.5923.39100.000914.643142640201014827
2016-17SAN5176.7618.33300.000813.6154121611416620
2017-18ATL5595.11831.58116.16756.8336232954611942
2019-20SAC4480.41022.455512.41735.6006212735415728

NBA Regular Season Stats - Advanced Stats

SeasonTeamGPGSTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2013-14All Teams316.500.45810.6926.4918.371.9515.640.904.4912.99111.41-3.701.17100.2105.610.55
2013-14ORL166.488.43413.6126.6520.021.3012.951.544.5613.59119.87-3.311.11103.8106.312.31
2013-14PHL110.533.5177.2427.9217.013.0220.59-4.6012.11105.57-4.411.2895.0105.28.60
2013-14GOS40.266.000-------13.9750.00-1.0062.8115.0-5.12
2014-15ORL5915.568.56215.1724.2219.551.6321.150.974.9112.95109.30-5.361.33106.4103.413.30
2015-16ORL5820.606.55910.2825.7117.842.7713.201.565.3615.18130.93-3.291.44116.5102.716.91
2016-17SAN7637.645.62211.1730.4421.014.8016.901.413.7112.24132.05-2.431.49121.898.515.94
2017-18ATL6246.599.5777.2327.5117.489.2414.311.262.8216.91165.78-1.661.29111.2106.815.67
2018-19ATL6452.602.5716.8425.3415.928.1512.731.993.8816.80168.90-1.441.32115.1109.016.22
2019-20All Teams4418.473.4438.4128.0917.964.1117.391.574.9418.31143.89-5.561.0090.1107.99.57
2019-20SAC3410.477.4449.2426.0917.364.0520.411.364.5918.56142.21-7.011.0288.2107.38.68
2019-20ATL108.463.4406.5032.1119.194.1710.252.055.5717.57149.01-2.270.9694.8110.811.63

NBA Regular Season Stats - Misc Stats

SeasonTeamGPGSDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2013-14All Teams316000000.0380.280.390.39724.2260.060.450.52
2013-14ORL166000000.0430.220.780.32214.1250.110.250.37
2013-14PHL110000000.0450.330.000.4538.273-0.040.180.15
2013-14GOS40000000.0000.000.002.0022.500-0.02--0.02
2014-15ORL5915200002.0460.180.310.401742.2880.681.101.78
2015-16ORL5820100001.0490.410.690.432137.3621.221.042.27
2016-17SAN7637600002.0690.720.610.365521.7242.233.015.24
2017-18ATL62461500001.1441.050.470.181844.2901.781.903.68
2018-19ATL64521100002.1331.070.820.212440.3752.121.904.02
2019-20All Teams4418300003.0690.390.440.141727.386-0.961.030.07
2019-20SAC3410300001.0620.320.320.161321.382-0.780.73-0.05
2019-20ATL108000002.0920.701.000.1046.400-0.160.230.07

NBA Regular Season Stats - Per Game

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14All Teams31612.61.232.68.4580.000.00.0000.681.03.6561.232.874.100.160.230.712.130.583.13
2013-14ORL16614.61.443.31.4340.000.00.0000.811.06.7651.693.194.880.120.440.812.310.563.69
2013-14PHL11013.71.362.64.5170.000.00.0000.641.18.5381.003.454.450.270.000.822.550.823.36
2013-14GOS401.40.000.25.0000.000.00.0000.250.50.5000.000.000.000.000.000.000.250.000.25
2014-15ORL591514.31.542.75.5620.000.02.0000.581.08.5312.003.005.000.150.270.852.390.863.66
2015-16ORL582012.21.713.05.5590.000.00.0000.981.31.7501.162.783.930.220.380.791.880.554.40
2016-17SAN763717.52.123.41.6220.000.00.0000.861.22.6991.704.836.530.580.490.802.370.805.09
2017-18ATL624624.94.037.69.5240.812.27.3551.081.39.7791.616.277.891.450.650.822.611.399.95
2018-19ATL645225.14.058.22.4921.303.39.3821.441.77.8141.645.867.501.411.081.113.341.3110.83
2019-20All Teams441817.62.325.80.4000.502.43.2060.680.82.8331.364.305.660.500.570.933.021.305.82
2019-20SAC341015.92.035.03.4040.412.09.1970.680.82.8211.353.564.910.440.440.762.741.385.15
2019-20ATL10823.33.308.40.3930.803.60.2220.700.80.8751.406.808.200.701.001.504.001.008.10

NBA Regular Season Stats - Totals

SeasonTeamGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14All Teams316390.43883.45800.0002132.65638891275722661897
2013-14ORL166234.32353.43400.0001317.765275178271337959
2013-14PHL110150.41529.51700.000713.53811384930928937
2013-14GOS405.701.00000.00012.500000000101
2014-15ORL5915845.191162.56201.0003464.5311181772959165014151216
2015-16ORL5820704.999177.55900.0005776.7506716122813224610932255
2016-17SAN76371329.7161259.62200.0006593.69912936749644376118061387
2017-18ATL62461542.3250477.52450141.3556786.77910038948990405116286617
2018-19ATL64521609.2259526.49283217.38292113.81410537548090697121484693
2019-20All Teams4418773.6102255.40022107.2063036.8336018924922254113357256
2019-20SAC3410540.569171.4041471.1972328.821461211671515269347175
2019-20ATL108233.13384.393836.22278.87514688271015401081

NBA Summer League Stats - Advanced Stats

SeasonTeamLocationGPGSTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2013-14All Teams-95.437.4249.9824.5516.941.2620.780.723.1813.54109.09-5.850.9183.196.56.35
2013-14DALLas Vegas63.476.46210.9026.8418.60-15.470.482.5713.90112.82-4.711.0094.096.29.47
2013-14MIAOrlando32.286.2866.6015.1510.584.4836.361.364.7612.3428.57-8.840.5750.797.6-2.07
2014-15ORLOrlando33.611.53310.5214.7612.908.2314.864.868.1219.16120.00-3.171.87118.387.124.96

NBA Summer League Stats - Misc Stats

SeasonTeamLocationGPGSDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2013-14All Teams-95000000.0590.110.220.0945.444-0.080.160.08
2013-14DALLas Vegas63000000.0690.000.200.1224.3330.050.120.18
2013-14MIAOrlando32000000.0370.250.250.0021.667-0.130.04-0.10
2014-15ORLOrlando33000000.1280.751.501.2021.6670.250.190.43

NBA Summer League Stats - Per Game

SeasonTeamLocationGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14All Teams-9514.71.563.67.4240.000.00.0000.220.33.6671.333.004.330.110.220.564.001.003.33
2013-14DALLas Vegas6316.12.004.33.4620.000.00.0000.330.50.6671.673.835.500.000.170.504.500.834.33
2013-14MIAOrlando3211.80.672.33.2860.000.00.0000.000.00.0000.671.332.000.330.330.673.001.331.33
2014-15ORLOrlando3320.82.675.00.5330.000.00.0004.006.00.6671.673.004.671.002.002.005.671.339.33

NBA Summer League Stats - Totals

SeasonTeamLocationGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2013-14All Teams-95132.41433.42400.00023.66712273912536930
2013-14DALLas Vegas6396.81226.46200.00023.66710233301327526
2013-14MIAOrlando3235.527.28600.00000.000246112944
2014-15ORLOrlando3362.5815.53300.0001218.667591436617428

NCAA Season Stats - Advanced Stats

SeasonSchoolClassGPTS%eFG%ORB%DRB%TRB%AST%TOV%STL%BLK%USG%Total S %PPRPPSORtgDRtgPER
2008-09Antelope Valley CollegeRS-Fr-----------------
2009-10Antelope Valley CollegeFr-----------------
2010-11N/ATrans-----------------
2011-12USCSo20.553.55112.6318.1115.153.5817.421.975.1020.35108.74-6.021.29102.693.718.07
2012-13USCJr31.524.50011.7824.1118.005.8819.812.9410.0618.53118.09-5.191.1898.288.418.93

NCAA Season Stats - Misc Stats

SeasonSchoolClassGPDbl DblTpl Dbl40 Pts20 Reb20 AstTechsHOBAst/TOStl/TOFT/FGAW'sL'sWin %OWSDWSWS
2008-09Antelope Valley CollegeRS-Fr-----------------
2009-10Antelope Valley CollegeFr-----------------
2010-11N/ATrans-----------------
2011-12USCSo20000001.1750.210.480.35416.2000.770.811.58
2012-13USCJr31200001.1430.390.690.271417.4520.661.682.34

NCAA Season Stats - Per Game

SeasonSchoolClassGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2008-09Antelope Valley CollegeRS-Fr---------------------
2009-10Antelope Valley CollegeFr---------------------
2010-11N/ATrans---------------------
2011-12USCSo201723.23.255.90.5510.000.05.0001.102.05.5372.453.005.450.300.700.953.201.457.60
2012-13USCJr312922.32.845.68.5000.000.10.0001.031.52.6812.264.716.970.611.102.133.001.586.71

NCAA Season Stats - Totals

SeasonSchoolClassGPGSMINFGMFGAFG%3PM3PA3P%FTMFTAFT%OFFDEFTRBASTSTLBLKPFTOVPTS
2008-09Antelope Valley CollegeRS-Fr---------------------
2009-10Antelope Valley CollegeFr---------------------
2010-11N/ATrans---------------------
2011-12USCSo201746565118.55101.0002241.5374960109614196429152
2012-13USCJr312969188176.50003.0003247.681701462161934669349208
* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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