306.Are players with higher match consistency always the ones winning major tournaments?A Complete Sas Study

Are players with higher match consistency always the ones winning major tournaments?A Complete Sas Study

1) Create master dataset

options validvarname=any nocenter;

data work.tennis_players;

  infile datalines dlm=',' dsd truncover;

  length PlayerID $4 Name $30 Country $20 Hand $1;

  format DOB ddmmyy10.;

  input PlayerID :$4. Name :$30. Country :$20. DOB :ddmmyy10. Height_cm Hand :$1.;

datalines;

P001,"Rafael Nadal","Spain",03-06-1986,185,R

P002,"Novak Djokovic","Serbia",22-05-1987,188,R

P003,"Roger Federer","Switzerland",08-08-1981,185,R

P004,"Andy Murray","United Kingdom",15-05-1987,190,R

P005,"Serena Williams","USA",26-09-1981,175,R

P006,"Naomi Osaka","Japan",16-10-1997,180,R

P007,"Ashleigh Barty","Australia",24-04-1996,166,R

P008,"Simona Halep","Romania",27-09-1991,168,R

P009,"Dominic Thiem","Austria",03-09-1993,185,R

P010,"Alexander Zverev","Germany",20-04-1997,198,R

P011,"Iga Swiatek","Poland",31-05-2001,175,R

P012,"Carlos Alcaraz","Spain",05-05-2003,185,R

;

run;

proc print data=work.tennis_players noobs; 

run;

OUTPUT:

PlayerID	Name	Country	Hand	DOB	Height_cm
P001	Rafael Nadal	Spain	R	03/06/1986	185
P002	Novak Djokovic	Serbia	R	22/05/1987	188
P003	Roger Federer	Switzerland	R	08/08/1981	185
P004	Andy Murray	United Kingdom	R	15/05/1987	190
P005	Serena Williams	USA	R	26/09/1981	175
P006	Naomi Osaka	Japan	R	16/10/1997	180
P007	Ashleigh Barty	Australia	R	24/04/1996	166
P008	Simona Halep	Romania	R	27/09/1991	168
P009	Dominic Thiem	Austria	R	03/09/1993	185
P010	Alexander Zverev	Germany	R	20/04/1997	198
P011	Iga Swiatek	Poland	R	31/05/2001	175
P012	Carlos Alcaraz	Spain	R	05/05/2003	185

2) DATA step derivations: Age (as of a fixed date), AgeGroup, Height_m

data work.tennis_players_deriv;

  set work.tennis_players;

  /* Reference date is explicit to make results reproducible */

  ref_date = '11NOV2025'd;

  /* Calculate integer age at ref_date accurately */

  years = intck('year', DOB, ref_date);

  /* Adjust if birthday hasn't occurred yet in reference year */

  if month(ref_date) < month(DOB) or (month(ref_date)=month(DOB) and 

  day(ref_date)<day(DOB)) then Age = years - 1;

  else Age = years;

  /* Height in meters */

  Height_m = round(Height_cm / 100, 0.01);

  /*  Age group */

  if Age < 20 then AgeGroup = 'Under20';

  else if 20 <= Age < 25 then AgeGroup = '20-24';

  else if 25 <= Age < 30 then AgeGroup = '25-29';

  else if 30 <= Age < 35 then AgeGroup = '30-34';

  else AgeGroup = '35plus';

  /* Drop helper variable years, keep ref_date for traceability */

  drop years;

run;

proc print data=work.tennis_players_deriv noobs; 

 var PlayerID Name Country DOB Age AgeGroup Height_cm Height_m Hand ref_date;

run;

OUTPUT:

PlayerID	Name	Country	DOB	Age	AgeGroup	Height_cm	Height_m	Hand	ref_date
P001	Rafael Nadal	Spain	03/06/1986	39	35plus	185	1.85	R	24056
P002	Novak Djokovic	Serbia	22/05/1987	38	35plus	188	1.88	R	24056
P003	Roger Federer	Switzerland	08/08/1981	44	35plus	185	1.85	R	24056
P004	Andy Murray	United Kingdom	15/05/1987	38	35plus	190	1.90	R	24056
P005	Serena Williams	USA	26/09/1981	44	35plus	175	1.75	R	24056
P006	Naomi Osaka	Japan	16/10/1997	28	25-29	180	1.80	R	24056
P007	Ashleigh Barty	Australia	24/04/1996	29	25-29	166	1.66	R	24056
P008	Simona Halep	Romania	27/09/1991	34	30-34	168	1.68	R	24056
P009	Dominic Thiem	Austria	03/09/1993	32	30-34	185	1.85	R	24056
P010	Alexander Zverev	Germany	20/04/1997	28	25-29	198	1.98	R	24056
P011	Iga Swiatek	Poland	31/05/2001	24	20-24	175	1.75	R	24056
P012	Carlos Alcaraz	Spain	05/05/2003	22	20-24	185	1.85	R	24056

3) PROC SQL summaries: counts, averages, country-level rollups

proc sql;

  create table work.country_summary as

  select Country,

         count(*) as NumPlayers,

         mean(Age) as AvgAge format=6.2,

         min(Age) as Youngest,

         max(Age) as Oldest,

         mean(Height_cm) as AvgHeight_cm format=6.1

  from work.tennis_players_deriv

  group by Country

  order by NumPlayers desc;

quit;

proc print data=work.country_summary noobs;

run;

OUTPUT:

Country	NumPlayers	AvgAge	Youngest	Oldest	AvgHeight_cm
Spain	2	30.50	22	39	185.0
Romania	1	34.00	34	34	168.0
Germany	1	28.00	28	28	198.0
Serbia	1	38.00	38	38	188.0
USA	1	44.00	44	44	175.0
Poland	1	24.00	24	24	175.0
United Kingdom	1	38.00	38	38	190.0
Australia	1	29.00	29	29	166.0
Austria	1	32.00	32	32	185.0
Japan	1	28.00	28	28	180.0
Switzerland	1	44.00	44	44	185.0

4) Create synthetic monthly ranking points time series for 3 players

/* Choose 3 players to demonstrate time-series: P002, P001, P012 */

data work.rankings;

  length PlayerID $4;

  format Month yymmn6.;

  /* Loop through player IDs */

  do PlayerID = 'P002','P001','P012';

    /* Assign a numeric ID for each player */

    select (PlayerID);

      when ('P002') player_num = 1;

      when ('P001') player_num = 2;

      when ('P012') player_num = 3;

      otherwise player_num = 0;

    end;

    /* Generate 12 months of data per player */

    do m = 0 to 11;

      Month = intnx('month', '01JAN2025'd, m, 'begin');

      /* Deterministic seed for reproducible randomness */

      seed = 12345 + player_num*1000 + m;

      call streaminit(seed);

      /* Different base points per player */

      base = 8000 + player_num*250;  

      /* Monthly seasonal and random variation */

      seasonal = 200 * sin(2*3.14159*m/12) + 50*mod(m,3);

      noise = round(rand('NORMAL', 0, 150), 1);

      /* Final player points */

      Points = round(base + seasonal + noise, 1);

      output;

    end;

  end;

  /* Drop intermediate variables */

  drop m seed base seasonal noise player_num;

run;

proc print data=work.rankings(obs=12) noobs;

run;

OUTPUT:

PlayerID	Month	Points
P002	202501	8281
P002	202502	8363
P002	202503	8752
P002	202504	8285
P002	202505	8585
P002	202506	8330
P002	202507	8127
P002	202508	8151
P002	202509	8147
P002	202510	7846
P002	202511	7933
P002	202512	8413

5) PROC EXPAND: compute 3-month and 6-month moving averages (SAS/ETS required)

proc sort data=work.rankings;

 by PlayerID Month;

run;

proc print data=work.rankings;

run;

OUTPUT:

Obs	PlayerID	Month	Points
1	P001	202501	8586
2	P001	202502	8507
3	P001	202503	8515
4	P001	202504	8618
5	P001	202505	8561
6	P001	202506	8671
7	P001	202507	8601
8	P001	202508	8215
9	P001	202509	8357
10	P001	202510	8174
11	P001	202511	8417
12	P001	202512	8366
13	P002	202501	8281
14	P002	202502	8363
15	P002	202503	8752
16	P002	202504	8285
17	P002	202505	8585
18	P002	202506	8330
19	P002	202507	8127
20	P002	202508	8151
21	P002	202509	8147
22	P002	202510	7846
23	P002	202511	7933
24	P002	202512	8413
25	P012	202501	8512
26	P012	202502	8507
27	P012	202503	9154
28	P012	202504	8888
29	P012	202505	8746
30	P012	202506	9161
31	P012	202507	8466
32	P012	202508	8588
33	P012	202509	8917
34	P012	202510	8585
35	P012	202511	8682
36	P012	202512	8861

proc expand data=work.rankings out=work.rankings_ma method=none;

  by PlayerID;

  id Month;

  convert Points = MA_3 / transformout=(movave 3);

  convert Points = MA_6 / transformout=(movave 6);

run;

proc print data=work.rankings_ma(obs=18) noobs;

run;

OUTPUT:

PlayerID	Month	MA_3	MA_6	Points
P001	202501	8586.00	8586.00	8586
P001	202502	8546.50	8546.50	8507
P001	202503	8536.00	8536.00	8515
P001	202504	8546.67	8556.50	8618
P001	202505	8564.67	8557.40	8561
P001	202506	8616.67	8576.33	8671
P001	202507	8611.00	8578.83	8601
P001	202508	8495.67	8530.17	8215
P001	202509	8391.00	8503.83	8357
P001	202510	8248.67	8429.83	8174
P001	202511	8316.00	8405.83	8417
P001	202512	8319.00	8355.00	8366
P002	202501	8281.00	8281.00	8281
P002	202502	8322.00	8322.00	8363
P002	202503	8465.33	8465.33	8752
P002	202504	8466.67	8420.25	8285
P002	202505	8540.67	8453.20	8585
P002	202506	8400.00	8432.67	8330

6) Plot time series and moving averages with PROC SGPLOT

proc sgpanel data=work.rankings_ma;

    panelby PlayerID / layout=panel onepanel;

    series x=Month y=Points / markers;

    series x=Month y=MA_3 / lineattrs=(pattern=2) legendlabel='3-Month MA';

    series x=Month y=MA_6 / lineattrs=(pattern=3) legendlabel='6-Month MA';

    rowaxis label="Ranking Points";

    colaxis label="Month";

    title "Ranking Points and Moving Averages by Player";

run;

OUTPUT:

7) Macro examples: 

- %derive_core(ds=, out=) : run same derivations on any dataset with DOB, Height_cm

- %player_movingavg(pid=, out=): generate moving average for a single player using PROC EXPAND

%macro derive_core(ds=work.tennis_players, out=);

  data &out.;

    set &ds.;

    ref_date='11NOV2025'd;

    years = intck('year', DOB, ref_date);

    if month(ref_date) < month(DOB) or (month(ref_date)=month(DOB) and day(ref_date)<day(DOB)) 

    then Age = years - 1;

    else Age = years;

    Height_m = round(Height_cm/100,0.01);

    if Age < 20 then AgeGroup='Under20';

    else if 20 <= Age < 25 then AgeGroup='20-24';

    else if 25 <= Age < 30 then AgeGroup='25-29';

    else if 30 <= Age < 35 then AgeGroup='30-34';

    else AgeGroup='35plus';

    drop years;

  run;

 proc print;run;

%mend derive_core;

%derive_core(ds=work.tennis_players, out=work.tennis_players_macro);

OUTPUT:

Obs	PlayerID	Name	Country	Hand	DOB	Height_cm	ref_date	Age	Height_m	AgeGroup
1	P001	Rafael Nadal	Spain	R	03/06/1986	185	24056	39	1.85	35plus
2	P002	Novak Djokovic	Serbia	R	22/05/1987	188	24056	38	1.88	35plus
3	P003	Roger Federer	Switzerland	R	08/08/1981	185	24056	44	1.85	35plus
4	P004	Andy Murray	United Kingdom	R	15/05/1987	190	24056	38	1.90	35plus
5	P005	Serena Williams	USA	R	26/09/1981	175	24056	44	1.75	35plus
6	P006	Naomi Osaka	Japan	R	16/10/1997	180	24056	28	1.80	25-29
7	P007	Ashleigh Barty	Australia	R	24/04/1996	166	24056	29	1.66	25-29
8	P008	Simona Halep	Romania	R	27/09/1991	168	24056	34	1.68	30-34
9	P009	Dominic Thiem	Austria	R	03/09/1993	185	24056	32	1.85	30-34
10	P010	Alexander Zverev	Germany	R	20/04/1997	198	24056	28	1.98	25-29
11	P011	Iga Swiatek	Poland	R	31/05/2001	175	24056	24	1.75	20-24
12	P012	Carlos Alcaraz	Spain	R	05/05/2003	185	24056	22	1.85	20-24

/* Macro to compute moving averages for a single player ID (convenience wrapper) */

%macro player_movingavg(pid=P002, out=work.pid_ma);

  data _tmp;

    set work.rankings;

    where PlayerID="&pid.";

  run;

  proc print;run;

  proc sort data=_tmp; 

  by Month; 

  run;

  proc expand data=_tmp out=&out. method=none;

    id Month;

    convert Points = MA_3 / transformout=(movave 3);

    convert Points = MA_6 / transformout=(movave 6);

  run;

%mend player_movingavg;

%player_movingavg(pid=P001, out=work.P001_ma);

OUTPUT:

Obs	PlayerID	Month	Points
1	P001	202501	8586
2	P001	202502	8507
3	P001	202503	8515
4	P001	202504	8618
5	P001	202505	8561
6	P001	202506	8671
7	P001	202507	8601
8	P001	202508	8215
9	P001	202509	8357
10	P001	202510	8174
11	P001	202511	8417
12	P001	202512	8366

proc print data=work.P001_ma noobs; 

run;

OUTPUT:

Month	MA_3	MA_6	PlayerID	Points
202501	8586.00	8586.00	P001	8586
202502	8546.50	8546.50	P001	8507
202503	8536.00	8536.00	P001	8515
202504	8546.67	8556.50	P001	8618
202505	8564.67	8557.40	P001	8561
202506	8616.67	8576.33	P001	8671
202507	8611.00	8578.83	P001	8601
202508	8495.67	8530.17	P001	8215
202509	8391.00	8503.83	P001	8357
202510	8248.67	8429.83	P001	8174
202511	8316.00	8405.83	P001	8417
202512	8319.00	8355.00	P001	8366

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