Airline True On Time Percentage

Now that we have the airport factors - which are perfectly correlated with on time percentage - we can use the same Department of Transportation (DOT) stats that we used in the previous post to break things down by airline. Doing this will allow us to calculate each airline's on time percentage for departed flights for each airport. After that, we simply adjust those numbers with the respective airport factor and recalculate.

I suggest reading the previous two posts to understand the airport factors.

Essentially, what we are doing here is attempting to build a context neutral stat. For example, we don't want a limitation of an airport to hinder the stats of the airline. Granted, airlines choose to fly in to these airports and they know what they are getting into from an operations standpoint prior to making the commitment. However, lots of times the profitability of the routes are weighted higher than the operation of the airport. Basically, what I am saying is that to understand the true performance of the airline operations we must strip out anything directly related to the overall performance of the airport, but to understand how an airline in general is performing we may want to keep this variable in place due to the fact the airline decided to fly in and out of that particular airport.

Below are the on time performance stats for each major US airline for all of 2015 and 2016. These are the stats the media outlets typically site when ranking the airlines' dependability.

	OT%
HA	0.938
AS	0.900
DL	0.857
AA	0.819
WN	0.808
UA	0.803
F9	0.773
NK	0.754
B6	0.750

 While these rankings are not entirely accurate they do generally get the list- at least from top half to bottom half - somewhat right. Now it is questionable whether these rankings that come out each year - and almost every news outlet reports on it (everyone loves to hate the airlines) - do anything to steer people to one airline over another. In my opinion, it does not. In reality a person's view of an airline is somewhat like political views. You can debate with a person until you are blue in the face, but if that other person has a preconceived notion of the airline in debate there is no swaying them in either direction. For example, if my friend had a bad experience with Delta - lets say his flight was delayed by four hours as he was trying to get to a wedding - and he saw CNN release a list of airlines that had Delta number one for best on time performance, he is most likely not going to switch his future plans to now include Delta as a potential transport. He is going to remember his recent terrible experience and steer clear. That is one theory. Another theory - and one that I tend to buy into even more - is that for a majority of domestic air travelers flights are a commodity. This means they are looking for the cheapest fare and will make their decision regardless of how any of the airlines have performed operationally in the past. If this assumption is true, and a traveler sees two identical fares with the same flight times, etc., then he or she may.... may.... be persuaded to go with an airline they had heard was better at delivering on their promise to depart/arrive on time.

So what does all of this have to do with our attempt to better rank airline on time performance?

Not much, which is the point. This is really a labor of love. Just like the current rankings that are published - I don't forsee this to motivate travelers to or from a certain airline. So after some tedious calculations here are what I am calling the True On Time Performance (T-OT%) stats for the same major US airlines.

	T-OT%
AS	0.895
DL	0.875
HA	0.874
AA	0.856
UA	0.844
WN	0.837
F9	0.803
NK	0.793
B6	0.789

As you can see the context neutral stats have brought on some slight changes to the rankings. Hawaiin went from number one to number two. Alaska jumped from two to one and Delta three to two. Southwest and United swapped spots and the bottom three (Frontier, Spirit and JetBlue) remained the same.

The easy one to explain is Hawaiin. They fly from Hawaiin airports more than any airline. Those airports are known for their great on time stats. Great weather has a lot to do with it, but they also have less traffic which always helps - less congestion at the gates and on the runways.

To understand how and why each airline's numbers shifted you would need to take a deep dive into the company's network of airports with the theory being that if an airline flies from more airports that have bad operational times than ones with gone times - that airline would see their numbers shoot up when going from looking at regular to true on time performance. Let's take the bottom three on the list for an example. All three (F9, NK, and B6) saw an increase to their true on time performance average. This makes me believe that these airlines have been flying from airports that are hard on operations. This makes sense when looking at Spirit and Frontier - both ULCC's - with costs so important. These airlines will fly to and from any destination where the costs are low and the load factors are high. Sometimes how well an aiport is run is not taken into consideration. I am not saying this is necessarily a bad thing, but just something to take into consideration when analyzing these results.

I hope to be able to write more about this at a later date.

Updated Airport Factors

In my last post I provided a list of airports and their calculated airport factor. This was the first step in adjusting airlines on time performance by stripping out the airport variable. The theory being that all airports are not created equal so why should we act like they are when analyzing airlines' on time metrics.

I was planning for this post to be about using the airport factors to calculate each airline's "true" on time performance, but instead I wanted to quickly post the updated airport factors that now include the year 2015 as well as 2016.

I made the decision to only pull airport/airline on time performance data going back two years because there can be significant outliers that affect the data set by going back further. For example, an airport could start construction on a runway(s) that could drive an increase in departure delays (think LAX currently). While this variable is acceptable in the short term, if you keep it in their for five years, other - more recent - variables could be having a greater impact on the numbers; however, you would never know it because it would be getting watered down by the old information. The main reason I did not want to use older data was because airline strategies can shift extremely quickly. If you were to pull data for say 5 years ago you would be seeing old data that may not even be relevant anymore. To be honest, I questioned going back two years, but one year's worth of data seemed to small so I settled on two years.

Without further ado, here are the updated airport factors using on time performance - from the department of transportation (DOT - for 2015 through 2016.

ORIGIN	On Time %	Airport Factor
LWS	0.961651917	116.3
HLN	0.939747925	113.6
PIH	0.937690898	113.3
EKO	0.932134096	112.7
ITO	0.922622951	111.5
BLI	0.92103265	111.3
GTF	0.920953576	111.3
BTM	0.920341394	111.2
BRD	0.912636505	110.3
YUM	0.911061286	110.1
LIH	0.910592182	110.1
BIL	0.910424946	110.0
BJI	0.908841672	109.8
TWF	0.905405405	109.4
SGU	0.905395418	109.4
GJT	0.905180534	109.4
GCC	0.904941176	109.4
HNL	0.903856969	109.2
IDA	0.903670655	109.2
CDC	0.903354633	109.2
KOA	0.898026719	108.5
CPR	0.897549454	108.5
YAK	0.8957902	108.3
RKS	0.893390192	108.0
BET	0.888151175	107.3
FCA	0.886921885	107.2
GFK	0.886113463	107.1
MSO	0.882555781	106.7
IMT	0.881180812	106.5
SIT	0.880926916	106.5
SCC	0.88079096	106.4
OGG	0.880710494	106.4
ANC	0.879540776	106.3
PSC	0.878623469	106.2
ABR	0.878114478	106.1
INL	0.874900398	105.7
LAR	0.873015873	105.5
RHI	0.871401152	105.3
ISN	0.871341048	105.3
HIB	0.871262038	105.3
HOB	0.869879518	105.1
FNT	0.86939679	105.1
LCH	0.869054786	105.0
ILM	0.868799656	105.0
FAI	0.867437539	104.8
LNK	0.867179659	104.8
ECP	0.866688756	104.7
HRL	0.866582558	104.7
CLL	0.866412214	104.7
CDV	0.866022099	104.7
MOT	0.865948856	104.6
GEG	0.865619632	104.6
LSE	0.86521231	104.6
LRD	0.864399293	104.5
EYW	0.863671572	104.4
BRW	0.863539446	104.3
ESC	0.863527534	104.3
BGM	0.863447127	104.3
SLC	0.862713695	104.2
BRO	0.861575179	104.1
DAB	0.861358575	104.1
JNU	0.85982242	103.9
ADQ	0.859632139	103.9
GRB	0.859589417	103.9
FLG	0.857532751	103.6
GPT	0.857466704	103.6
MLB	0.857383682	103.6
HSV	0.856426821	103.5
BZN	0.855189255	103.3
ELM	0.85340186	103.1
BFL	0.853033376	103.1
AVP	0.852866242	103.1
MSN	0.851985147	102.9
BIS	0.851587816	102.9
ATW	0.851572133	102.9
PIB	0.851556265	102.9
PDX	0.850993618	102.8
GRK	0.850331544	102.7
AMA	0.850184308	102.7
GTR	0.849605523	102.7
TLH	0.849362688	102.6
PIT	0.848801117	102.6
BMI	0.848383501	102.5
AZO	0.848170129	102.5
LAN	0.848168701	102.5
EWN	0.847597598	102.4
BQK	0.847117794	102.4
CAK	0.846418675	102.3
ITH	0.846318036	102.3
MFE	0.845027248	102.1
MHT	0.844488624	102.0
ALB	0.844316498	102.0
HYS	0.844249201	102.0
JAN	0.843807906	102.0
CWA	0.843525938	101.9
MQT	0.843232717	101.9
VLD	0.843003413	101.9
FSM	0.842965204	101.9
KTN	0.842168675	101.8
LBB	0.841745259	101.7
ABY	0.84173028	101.7
TUL	0.840670788	101.6
MDT	0.839516261	101.4
CIU	0.839233038	101.4
RAP	0.838893159	101.4
FAR	0.83826991	101.3
TRI	0.838041431	101.3
DRO	0.837927757	101.2
MSP	0.837716078	101.2
BOI	0.837287889	101.2
CSG	0.837267081	101.2
OMA	0.836945418	101.1
MOB	0.83671106	101.1
PNS	0.83592547	101.0
OTZ	0.835862069	101.0
COD	0.835497835	100.9
GRR	0.834953307	100.9
RDM	0.834792761	100.9
MLU	0.834785364	100.9
PLN	0.834551148	100.8
BGR	0.834331337	100.8
OAJ	0.834326579	100.8
LFT	0.834005764	100.8
LEX	0.833674165	100.7
MAF	0.833619456	100.7
SEA	0.83340685	100.7
PVD	0.832910679	100.6
PAH	0.83286119	100.6
APN	0.832807571	100.6
TUS	0.832733536	100.6
VPS	0.832297482	100.6
DVL	0.832054561	100.5
EAU	0.831564048	100.5
CRP	0.831083546	100.4
AGS	0.831004785	100.4
BUF	0.83097316	100.4
BDL	0.830957629	100.4
GSP	0.830914166	100.4
SYR	0.830324017	100.3
MKE	0.830309646	100.3
FWA	0.829483832	100.2
OKC	0.82942055	100.2
PIA	0.829214039	100.2
ERI	0.829032258	100.2
COS	0.829024081	100.2
DLH	0.828733032	100.1
RST	0.828405797	100.1
ICT	0.828300414	100.1
LIT	0.828172985	100.1
LAW	0.828131021	100.1
WRG	0.828060523	100.0
MLI	0.827888687	100.0
ABE	0.827734535	100.0
BHM	0.827657935	100.0
SPI	0.827625899	100.0
PSG	0.827372765	100.0
MCI	0.826910303	99.9
SNA	0.826721561	99.9
PHF	0.826677316	99.9
TYR	0.825909555	99.8
AEX	0.825699746	99.8
DAY	0.825641992	99.8
CHA	0.825553626	99.7
SAT	0.825540592	99.7
EVV	0.825039438	99.7
IND	0.824810589	99.7
TVC	0.824629023	99.6
BUR	0.824556528	99.6
DTW	0.82412618	99.6
FSD	0.823805717	99.5
MEM	0.823262542	99.5
ROC	0.823238487	99.5
JAX	0.823238373	99.5
SBN	0.822841992	99.4
ELP	0.822655448	99.4
FAY	0.822617681	99.4
MBS	0.822446864	99.4
PSE	0.821983274	99.3
JLN	0.821551724	99.3
MRY	0.821496788	99.3
SDF	0.820399219	99.1
PSP	0.820140966	99.1
MTJ	0.820123398	99.1
CVG	0.819869811	99.1
TXK	0.819364914	99.0
SCE	0.819080861	99.0
MSY	0.81890295	98.9
ROW	0.8187251	98.9
PPG	0.818565401	98.9
FAT	0.818250047	98.9
CMH	0.818022093	98.8
SBP	0.817822384	98.8
DHN	0.817785844	98.8
CAE	0.81714742	98.7
ACT	0.816923561	98.7
CLE	0.816723208	98.7
MFR	0.816182938	98.6
CHS	0.816107722	98.6
GNV	0.816102998	98.6
DSM	0.815957825	98.6
ORF	0.815429962	98.5
SGF	0.815407129	98.5
DCA	0.815251081	98.5
STX	0.814535158	98.4
SHV	0.814444444	98.4
EUG	0.813817018	98.3
CMX	0.81377899	98.3
CID	0.813451777	98.3
ISP	0.812341505	98.1
AVL	0.811984436	98.1
AUS	0.811843558	98.1
ATL	0.810751258	97.9
JAC	0.810263158	97.9
CLT	0.809916943	97.8
RSW	0.809495254	97.8
JMS	0.809419496	97.8
MGM	0.808736718	97.7
SAN	0.808368748	97.7
STT	0.808153477	97.6
SJC	0.80749806	97.6
ONT	0.807022001	97.5
GSO	0.806434796	97.4
ROA	0.806102988	97.4
RDU	0.805490746	97.3
SRQ	0.805347901	97.3
SMF	0.805009075	97.3
RNO	0.804637047	97.2
TYS	0.804578297	97.2
ABQ	0.804177611	97.2
PHX	0.803849314	97.1
XNA	0.803349372	97.1
LGB	0.803273149	97.0
OME	0.802622498	97.0
SBA	0.801989654	96.9
MEI	0.801815431	96.9
TPA	0.801598365	96.8
PWM	0.800208207	96.7
HDN	0.799199085	96.5
PHL	0.797630766	96.4
BNA	0.796440554	96.2
SMX	0.795791115	96.1
RIC	0.794685697	96.0
BTR	0.794581013	96.0
IAD	0.794009005	95.9
CHO	0.79380805	95.9
BTV	0.791779707	95.6
BOS	0.791629575	95.6
STL	0.790345073	95.5
SJU	0.789534797	95.4
EGE	0.788746631	95.3
SAV	0.786721618	95.0
CRW	0.78666925	95.0
IAH	0.785434032	94.9
RDD	0.782022472	94.5
TTN	0.780099693	94.2
MCO	0.777471678	93.9
SUN	0.776408451	93.8
SWF	0.77628935	93.8
OAK	0.773612469	93.4
ACY	0.77359244	93.4
SAF	0.773228907	93.4
LAS	0.770609162	93.1
DEN	0.769836962	93.0
ORH	0.769709544	93.0
MYR	0.769022719	92.9
IAG	0.76819407	92.8
FLL	0.767369411	92.7
MKG	0.764872521	92.4
LBE	0.764320786	92.3
JFK	0.764095765	92.3
DFW	0.76409194	92.3
SFO	0.763272114	92.2
BPT	0.763239875	92.2
GUM	0.762295082	92.1
HPN	0.760201413	91.8
PBI	0.758786008	91.7
HOU	0.757304302	91.5
LAX	0.755009591	91.2
BWI	0.754491144	91.1
GUC	0.753721245	91.0
DAL	0.753127972	91.0
LGA	0.747289563	90.3
MIA	0.745931552	90.1
MDW	0.745751849	90.1
ORD	0.737542547	89.1
EWR	0.734770685	88.7
ACV	0.733823015	88.6
BQN	0.731803081	88.4
PBG	0.730827068	88.3
ASE	0.711872646	86.0
UST	0.711538462	85.9
MMH	0.637362637	77.0
OTH	0.633387889	76.5
ADK	0.588516746	71.0

Airport Factors

From time to time, news agencies and government regulators will march out on time performance rankings of US airlines. These same outlets will have you believe that the airlines at the top of the list are so much better than those at the bottom. For the most part, these insinuations are not inaccurate – at least in the time frame the metrics were analyzed. However, there is something that I have never actually seen taken into consideration with these lists that I believe could change the perception of airlines; that being the airport variable. We all know there are certain airports that are just prone to delays and cancellations. I’m sure you could name one off the top of your head. This could be due to many factors including an above average risk of bad weather, large passenger volumes and airport capacity constraints.

Based on this assumption - that not all airports are created equal – it would only be fair to adjust these rankings based on the airports that each airline flies from. So that is what I intend to do.

For this initial research, I only looked at the year 2016. I plan on including multiple years soon. I used data provided by the Department of Transportation (DOT) – specifically departure on time performance numbers. The data set allowed me to break down the numbers by airport. A quick calculation gave me the percentage of on time departures from each airport in 2016. I then used those numbers to create an airport factor that can be used to adjust average on time performance numbers for each individual airline. By design, the airport factor and the percentage of on time departures from each airport are perfectly correlated.

Below is a list of 294 domestic airports and their average on time departure percentage as well as the corresponding airport factor for 2016. My next post will be about using these airport factors to adjust individual carriers on time performance statistics.

Airports with factors above 100 are those where more overall on time departures occur than those - at a combined average - of the other airports.

ORIGIN	On Time %	Airport Factor
LWS	0.954	112.7
HLN	0.948	112.0
BET	0.934	110.3
EKO	0.931	110.0
PIH	0.930	109.9
ITO	0.930	109.8
GJT	0.929	109.8
TYR	0.928	109.6
BTM	0.926	109.4
GTF	0.925	109.2
LIH	0.924	109.2
SCC	0.923	109.1
BIL	0.923	109.1
GCC	0.923	109.1
SGU	0.922	108.9
CPR	0.922	108.9
ANC	0.921	108.8
HNL	0.919	108.6
YUM	0.919	108.6
BLI	0.917	108.4
CDC	0.917	108.3
RKS	0.915	108.1
YAK	0.913	107.9
FCA	0.909	107.4
SIT	0.909	107.4
IDA	0.909	107.3
JNU	0.906	107.0
LSE	0.905	106.9
BRD	0.905	106.9
KOA	0.904	106.7
ABR	0.903	106.7
CDV	0.902	106.6
BJI	0.901	106.5
OGG	0.900	106.3
ADQ	0.899	106.1
FAI	0.898	106.1
BRW	0.897	105.9
AMA	0.896	105.8
GRK	0.894	105.6
OTZ	0.894	105.6
FNT	0.893	105.5
TWF	0.891	105.2
MSO	0.890	105.2
ISN	0.890	105.1
GFK	0.889	105.1
EYW	0.889	105.0
COD	0.885	104.5
PSG	0.883	104.3
KTN	0.883	104.3
BFL	0.883	104.3
GEG	0.882	104.2
MOT	0.882	104.2
LCH	0.882	104.2
HSV	0.881	104.1
ECP	0.881	104.1
FSM	0.880	103.9
BRO	0.879	103.8
ATW	0.878	103.7
LNK	0.878	103.7
MAF	0.878	103.7
ELM	0.877	103.6
GRB	0.877	103.6
ILM	0.876	103.5
PSC	0.876	103.5
CLL	0.876	103.4
BMI	0.875	103.4
FLG	0.875	103.4
CRP	0.875	103.4
SLC	0.875	103.4
IMT	0.874	103.3
BIS	0.874	103.3
MLI	0.874	103.2
GPT	0.873	103.1
HOB	0.873	103.1
INL	0.873	103.1
LAR	0.872	103.0
TLH	0.872	103.0
MFE	0.872	103.0
LBB	0.872	103.0
TUL	0.871	102.9
RHI	0.871	102.9
EAU	0.871	102.9
BZN	0.871	102.9
LIT	0.870	102.8
MSN	0.870	102.8
MLB	0.870	102.7
WRG	0.870	102.7
AVP	0.869	102.7
PNS	0.869	102.6
MHT	0.869	102.6
LRD	0.869	102.6
RAP	0.868	102.6
PIT	0.868	102.5
JAN	0.868	102.5
ICT	0.867	102.4
EWN	0.866	102.3
OME	0.866	102.3
HRL	0.866	102.2
PHF	0.865	102.2
AZO	0.863	102.0
BGM	0.862	101.8
DAB	0.862	101.8
CIU	0.862	101.8
PDX	0.861	101.7
PIB	0.861	101.7
RST	0.860	101.6
BOI	0.860	101.6
VPS	0.860	101.6
OKC	0.860	101.5
PPG	0.860	101.5
GRR	0.859	101.5
FAR	0.859	101.5
ROW	0.859	101.5
OMA	0.858	101.4
SGF	0.858	101.3
BQK	0.858	101.3
ALB	0.857	101.3
ABY	0.857	101.3
MQT	0.857	101.2
HYS	0.856	101.1
CID	0.856	101.1
FSD	0.856	101.1
JLN	0.856	101.0
SEA	0.855	101.0
PVD	0.855	101.0
XNA	0.854	100.9
DRO	0.854	100.8
CWA	0.854	100.8
BDL	0.853	100.8
VLD	0.853	100.8
ITH	0.853	100.7
MSP	0.853	100.7
LFT	0.852	100.7
DAY	0.852	100.6
LAN	0.852	100.6
CMH	0.852	100.6
GTR	0.852	100.6
MKE	0.852	100.6
OAJ	0.852	100.6
MLU	0.852	100.6
FWA	0.851	100.6
HIB	0.851	100.5
DSM	0.850	100.4
GUM	0.850	100.4
LAW	0.850	100.4
PIA	0.850	100.4
CVG	0.849	100.3
TRI	0.849	100.3
MCI	0.849	100.3
MOB	0.849	100.2
ESC	0.848	100.2
BUF	0.848	100.1
MEM	0.848	100.1
BHM	0.847	100.1
ISP	0.847	100.1
STX	0.847	100.1
CAK	0.847	100.0
DVL	0.846	100.0
SAT	0.845	99.8
DTW	0.844	99.7
SHV	0.844	99.7
MDT	0.843	99.6
AEX	0.843	99.6
JAX	0.843	99.5
STT	0.843	99.5
IND	0.843	99.5
ABE	0.843	99.5
APN	0.841	99.3
CHA	0.841	99.3
FAT	0.841	99.3
AGS	0.841	99.3
GSP	0.840	99.2
DLH	0.840	99.2
TUS	0.840	99.2
MBS	0.840	99.2
EVV	0.839	99.1
JMS	0.839	99.1
TYS	0.839	99.1
SNA	0.838	99.0
COS	0.838	99.0
CSG	0.838	99.0
PSE	0.838	98.9
FAY	0.838	98.9
PAH	0.838	98.9
MRY	0.837	98.9
CAE	0.837	98.9
AUS	0.836	98.8
ROC	0.836	98.8
IAD	0.836	98.7
PLN	0.836	98.7
SDF	0.835	98.6
SBN	0.835	98.6
LEX	0.834	98.6
SPI	0.834	98.5
ELP	0.834	98.5
SYR	0.834	98.5
ERI	0.834	98.5
MSY	0.834	98.5
ATL	0.833	98.4
GNV	0.833	98.4
SAN	0.831	98.2
SBP	0.831	98.1
PHX	0.831	98.1
ABQ	0.830	98.0
CLE	0.830	98.0
CLT	0.829	97.9
ORF	0.829	97.9
ACT	0.828	97.8
MTJ	0.827	97.7
SCE	0.827	97.7
TPA	0.827	97.7
DCA	0.826	97.5
RDU	0.825	97.5
PSP	0.825	97.5
GSO	0.825	97.4
IAH	0.825	97.4
ONT	0.824	97.4
ROA	0.824	97.4
BUR	0.823	97.3
RDM	0.823	97.3
CHS	0.823	97.2
MGM	0.823	97.2
PHL	0.823	97.1
RNO	0.821	96.9
RSW	0.821	96.9
HDN	0.820	96.9
JAC	0.820	96.9
BNA	0.820	96.9
DHN	0.820	96.8
SJC	0.819	96.8
TTN	0.819	96.8
SJU	0.818	96.6
TXK	0.816	96.4
EGE	0.816	96.4
SMF	0.816	96.3
TVC	0.816	96.3
CHO	0.816	96.3
MEI	0.816	96.3
AVL	0.815	96.3
MFR	0.815	96.2
BGR	0.814	96.2
EUG	0.814	96.1
BTR	0.814	96.1
LGB	0.812	95.9
PWM	0.812	95.9
LBE	0.812	95.9
BOS	0.812	95.8
RIC	0.808	95.5
STL	0.808	95.4
SRQ	0.806	95.2
GUC	0.806	95.2
BWI	0.806	95.1
DEN	0.805	95.0
SBA	0.804	95.0
MCO	0.804	94.9
CMX	0.802	94.7
UST	0.800	94.5
CRW	0.799	94.4
SAV	0.799	94.3
HOU	0.796	94.0
LAS	0.796	94.0
MYR	0.793	93.7
DFW	0.793	93.7
OAK	0.792	93.6
MDW	0.791	93.5
BTV	0.789	93.2
BPT	0.787	92.9
DAL	0.784	92.6
HPN	0.781	92.2
PBG	0.779	92.0
RDD	0.777	91.7
JFK	0.777	91.7
FLL	0.776	91.6
ACY	0.776	91.6
SWF	0.776	91.6
SUN	0.774	91.4
SFO	0.774	91.4
MKG	0.773	91.3
SMX	0.772	91.2
MIA	0.772	91.1
PBI	0.771	91.1
LGA	0.771	91.0
ADK	0.769	90.8
ORD	0.768	90.7
LAX	0.763	90.1
SAF	0.761	89.8
EWR	0.756	89.2
ORH	0.750	88.6
ASE	0.747	88.2
ACV	0.746	88.1
BQN	0.745	88.0
IAG	0.744	87.8
OTH	0.663	78.3
MMH	0.590	69.6